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{{short description|List of definitions of terms and concepts commonly used in the study of artificial intelligence}}
{{Use dmy dates|date=September 2017}}

This '''glossary of artificial intelligence''' is a list of definitions of terms and concepts relevant to the study of [[artificial intelligence]] (AI), its subdisciplines, and related fields. Related glossaries include [[Glossary of computer science]], [[Glossary of robotics]], and [[Glossary of machine vision]].

{{Compact TOC|side=yes|center=yes|nobreak=yes|seealso=yes|refs=yes|}}

{{Artificial intelligence}}

==A==
{{glossary}}
{{anchor|A* search}}{{term|[[A* search algorithm|A* search]]}}
{{ghat|Pronounced "A-star".}}
{{defn|A {{gli|graph traversal}} and {{gli|pathfinding}} {{gli|algorithm}} which is used in many fields of {{gli|computer science}} due to its completeness, optimality, and optimal efficiency.}}

{{term|[[abductive logic programming]] (ALP)}}
{{defn|A high-level knowledge-representation framework that can be used to solve problems declaratively based on {{gli|abductive reasoning}}. It extends normal {{gli|logic programming}} by allowing some predicates to be incompletely defined, declared as abducible predicates.}}

{{term|[[abductive reasoning]]}}
{{ghat|Also '''abduction'''.}}
{{defn|A form of [[logical inference]] which starts with an observation or set of observations then seeks to find the simplest and most likely explanation. This process, unlike [[deductive reasoning]], yields a plausible conclusion but does not [[logical positivism|positively verify]] it.<ref name="Josephson">For example: {{Cite book |title=Abductive Inference: Computation, Philosophy, Technology |date=1994 |publisher=Cambridge University Press |isbn=978-0521434614 |editor-last=Josephson |editor-first=John R. |location=Cambridge, UK; New York |doi=10.1017/CBO9780511530128 |oclc=28149683 |editor-last2=Josephson |editor-first2=Susan G.}}</ref> abductive inference,<ref name="Josephson" /> or retroduction<ref>{{Cite web |url=https://commens.org/dictionary/term/retroduction |title=Retroduction |website=Commens – Digital Companion to C. S. Peirce |publisher=Mats Bergman, Sami Paavola & João Queiroz |access-date=2014-08-24 |archive-date=5 July 2022 |archive-url=https://web.archive.org/web/20220705094016/http://www.commens.org/dictionary/term/retroduction |url-status=dead }}</ref>}}

{{term|[[Ablation (artificial intelligence)|ablation]]}}
{{defn|The removal of a component of an AI system. An ''ablation study'' aims to determine the contribution of a component to an AI system by removing the component, and then analyzing the resultant performance of the system.<ref>{{Cite book |last=Sheikholeslami |first=Sina |url=https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-258413 |title=Ablation Programming for Machine Learning |date=2019}}</ref>}}

{{term|[[abstract data type]]}}
{{defn|A [[mathematical model]] for [[data type]]s, where a data type is defined by its behavior ([[Semantics (computer science)|semantic]]s) from the point of view of a ''user'' of the data, specifically in terms of possible values, possible operations on data of this type, and the behavior of these operations.}}

{{term|[[abstraction (software engineering)|abstraction]]}}
{{defn|The process of removing physical, spatial, or temporal details<ref>{{Cite journal |last1=Colburn |first1=Timothy |last2=Shute |first2=Gary |date=2007-06-05 |title=Abstraction in Computer Science |journal=Minds and Machines |volume=17 |issue=2 |pages=169–184 |doi=10.1007/s11023-007-9061-7 |s2cid=5927969 |issn=0924-6495}}</ref> or [[Attribute (computing)|attribute]]s in the study of objects or [[system]]s in order to more closely attend to other details of interest<ref>{{Cite journal |last1=Kramer |first1=Jeff |date=2007-04-01 |title=Is abstraction the key to computing? |journal=Communications of the ACM |volume=50 |issue=4 |pages=36–42 |citeseerx=10.1.1.120.6776 |doi=10.1145/1232743.1232745 |s2cid=12481509 |issn=0001-0782}}</ref>}}

{{term|[[accelerating change]]}}
{{defn|A perceived increase in the rate of [[technological change]] throughout history, which may suggest faster and more profound change in the future and may or may not be accompanied by equally profound social and cultural change.}}

{{term|[[action language]]}}
{{defn|A language for specifying [[state transition system]]s, and is commonly used to create [[formal model]]s of the effects of actions on the world.<ref>Michael Gelfond, Vladimir Lifschitz (1998) "[https://ep.liu.se/ea/cis/1998/016/ Action Languages]", ''Linköping Electronic Articles in Computer and Information Science'', vol 3, nr ''16''.</ref> Action languages are commonly used in the {{gli|artificial intelligence}} and [[robotics]] domains, where they describe how actions affect the states of systems over time, and may be used for [[automated planning]].}}

{{term|[[action model learning]]}}
{{defn|An area of {{gli|machine learning}} concerned with creation and modification of software agent's knowledge about effects and preconditions of the actions that can be executed within its environment. This knowledge is usually represented in logic-based action description language and used as the input for automated planners.}}

{{term|[[action selection]]}}
{{defn|A way of characterizing the most basic problem of intelligent systems: what to do next. In artificial intelligence and computational cognitive science, "the action selection problem" is typically associated with intelligent agents and animats—artificial systems that exhibit complex behaviour in an agent environment.}}

{{term|[[activation function]]}}
{{defn|In {{gli|artificial neural network|artificial neural networks}}, the activation function of a node defines the output of that node given an input or set of inputs.}}

{{term|[[adaptive algorithm]]}}
{{defn|An algorithm that changes its behavior at the time it is run, based on ''a priori'' defined reward mechanism or criterion.}}

{{term|[[adaptive neuro fuzzy inference system]] (ANFIS)}}
{{ghat|Also '''adaptive network-based fuzzy inference system'''.}}
{{defn|A kind of {{gli|artificial neural network}} that is based on Takagi–Sugeno fuzzy [[inference system]]. The technique was developed in the early 1990s.<ref>{{Cite conference |last1=Jang |first1=Jyh-Shing R |year=1991 |title=Fuzzy Modeling Using Generalized Neural Networks and Kalman Filter Algorithm |url=https://aaai.org/Papers/AAAI/1991/AAAI91-119.pdf |conference=Proceedings of the 9th National Conference on Artificial Intelligence, Anaheim, CA, USA, July 14–19 |volume=2 |pages=762–767}}</ref><ref>{{Cite journal |last1=Jang |first1=J.-S.R. |s2cid=14345934 |year=1993 |title=ANFIS: adaptive-network-based fuzzy inference system |journal=IEEE Transactions on Systems, Man, and Cybernetics |volume=23 |issue=3 |pages=665–685 |doi=10.1109/21.256541}}</ref> Since it integrates both neural networks and {{gli|fuzzy logic}} principles, it has potential to capture the benefits of both in a single [[:wikt:framework|framework]]. Its inference system corresponds to a set of fuzzy [[Conditional (programming)|IF–THEN rule]]s that have learning capability to approximate nonlinear functions.<ref>{{Citation |last1=Abraham |first1=A. |title=Fuzzy Systems Engineering: Theory and Practice |volume=181 |pages=53–83 |year=2005 |editor-last=Nedjah |editor-first=Nadia |series=Studies in Fuzziness and Soft Computing |chapter=Adaptation of Fuzzy Inference System Using Neural Learning |place=Germany |publisher=Springer Verlag |citeseerx=10.1.1.161.6135 |doi=10.1007/11339366_3 |isbn=978-3-540-25322-8 |editor2-last=De Macedo Mourelle |editor2-first=Luiza}}</ref> Hence, ANFIS is considered to be a universal estimator.<ref>Jang, Sun, Mizutani (1997) – Neuro-Fuzzy and Soft Computing – Prentice Hall, pp 335–368, {{ISBN|0-13-261066-3}}</ref> For using the ANFIS in a more efficient and optimal way, one can use the best parameters obtained by genetic algorithm.<ref>{{Cite journal |last1=Tahmasebi |first1=P. |year=2012 |title=A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation |journal=Computers & Geosciences |volume=42 |pages=18–27 |bibcode=2012CG.....42...18T |doi=10.1016/j.cageo.2012.02.004 |pmc=4268588 |pmid=25540468}}</ref><ref>{{Cite journal |last1=Tahmasebi |first1=P. |year=2010 |title=Comparison of optimized neural network with fuzzy logic for ore grade estimation |url=https://www.researchgate.net/publication/266881168 |journal=Australian Journal of Basic and Applied Sciences |volume=4 |pages=764–772}}</ref>}}

{{term|[[admissible heuristic]]}}
{{defn|In [[computer science]], specifically in {{gli|algorithm|algorithms}} related to {{gli|pathfinding}}, a [[heuristic function]] is said to be admissible if it never overestimates the cost of reaching the goal, i.e. the cost it estimates to reach the goal is not higher than the lowest possible cost from the current point in the path.<ref>{{Cite book |last1=Russell |first1=S.J. |title=Artificial Intelligence: A Modern Approach |title-link=Artificial Intelligence: A Modern Approach |last2=Norvig, P. |publisher=Prentice Hall |year=2002 |isbn=978-0-13-790395-5}}</ref>}}

{{term|[[affective computing]]}}
{{ghat|Also '''artificial emotional intelligence''' or '''emotion AI'''.}}
{{defn|The study and development of systems and devices that can recognize, interpret, process, and simulate human [[Affect (psychology)|affect]]s. Affective computing is an interdisciplinary field spanning [[computer science]], [[psychology]], and [[cognitive science]].<ref name="TaoTan">{{Cite conference |last1=Tao |first1=Jianhua |last2=Tieniu Tan |year=2005 |title=Affective Computing: A Review |publisher=Springer |volume=[[LNCS]] 3784 |pages=981–995 |doi=10.1007/11573548 |book-title=Affective Computing and Intelligent Interaction}}</ref><ref>{{Cite news |last1=El Kaliouby |first1=Rana |url=https://technologyreview.com/s/609071/we-need-computers-with-empathy/ |archive-url=https://wayback.archive-it.org/all/20180707140902/https://technologyreview.com/s/609071/we-need-computers-with-empathy/ |url-status=dead |archive-date=7 July 2018 |title=We Need Computers with Empathy |date=Nov–Dec 2017 |work=Technology Review |issue=6 |volume=120 |page=8 |access-date=6 November 2018 }}</ref>}}

{{term|[[agent architecture]]}}
{{defn|A [[blueprint]] for [[software agent]]s and {{gli|intelligent control}} systems, depicting the arrangement of components. The architectures implemented by {{gli|intelligent agent|intelligent agents}} are referred to as [[cognitive architecture]]s.<ref>[https://hri.cogs.indiana.edu/publications/aaai04ws.pdf Comparison of Agent Architectures] {{webarchive |url=https://web.archive.org/web/20080827222057/https://hri.cogs.indiana.edu/publications/aaai04ws.pdf |date=August 27, 2008 }}</ref>}}

{{term|[[AI accelerator]]}}
{{defn|A class of [[microprocessor]]<ref>{{Cite web |url=https://v3.co.uk/v3-uk/news/3014293/intel-unveils-movidius-compute-stick-usb-ai-accelerator |title=Intel unveils Movidius Compute Stick USB AI Accelerator |date=2017-07-21 |url-status=dead |archive-url=https://web.archive.org/web/20170811193632/https://v3.co.uk/v3-uk/news/3014293/intel-unveils-movidius-compute-stick-usb-ai-accelerator |archive-date=11 August 2017 |access-date=28 November 2018}}</ref> or computer system<ref>{{Cite web |url=https://insidehpc.com/2017/06/inspurs-unveils-gx4-ai-accelerator/ |title=Inspurs unveils GX4 AI Accelerator |date=2017-06-21}}</ref> designed as [[hardware acceleration]] for {{gli|artificial intelligence}} applications, especially {{gli|artificial neural network|artificial neural networks}}, {{gli|machine vision}}, and {{gli|machine learning}}.}}

{{term|[[AI-complete]]}}
{{defn|In the field of artificial intelligence, the most difficult problems are informally known as AI-complete or AI-hard, implying that the difficulty of these computational problems is equivalent to that of solving the central artificial intelligence problem—making computers as intelligent as people, or {{gli|artificial general intelligence|strong AI}}.<ref name="Shapiro92">Shapiro, Stuart C. (1992). [https://cse.buffalo.edu/~shapiro/Papers/ai.pdf Artificial Intelligence] In Stuart C. Shapiro (Ed.), ''Encyclopedia of Artificial Intelligence'' (Second Edition, pp.&nbsp;54–57). New York: John Wiley. (Section 4 is on "AI-Complete Tasks".)</ref> To call a problem AI-complete reflects an attitude that it would not be solved by a simple specific algorithm.}}

{{term|[[algorithm]]}}
{{defn|An unambiguous specification of how to solve a class of problems. Algorithms can perform calculation, data processing, and automated reasoning tasks.}}

{{term|[[algorithmic efficiency]]}}
{{defn|A property of an {{gli|algorithm}} which relates to the number of [[computational resource]]s used by the algorithm. An algorithm must be [[analysis of algorithms|analyzed]] to determine its resource usage, and the efficiency of an algorithm can be measured based on usage of different resources. Algorithmic efficiency can be thought of as analogous to engineering [[productivity]] for a repeating or continuous process.}}

{{term|[[algorithmic probability]]}}
{{defn|In [[algorithmic information theory]], algorithmic probability, also known as Solomonoff probability, is a mathematical method of assigning a prior [[probability]] to a given observation. It was invented by [[Ray Solomonoff]] in the 1960s.<ref>Solomonoff, R., "[https://world.std.com/~rjs/z138.pdf A Preliminary Report on a General Theory of Inductive Inference]", Report V-131, Zator Co., Cambridge, Ma. (Nov. 1960 revision of the Feb. 4, 1960 report).</ref>}}

{{term|[[AlphaGo]]}}
{{defn|A [[computer program]] that plays the [[board game]] [[Go (game)|Go]].<ref>{{Cite news |url=https://bbc.com/news/technology-35785875 |title=Artificial intelligence: Google's AlphaGo beats Go master Lee Se-dol |date=2016-03-12 |work=BBC News |access-date=17 March 2016}}</ref> It was developed by [[Alphabet Inc.]]'s [[Google DeepMind]] in London. AlphaGo has several versions including [[AlphaGo Zero]], [[AlphaGo Master]], [[AlphaGo Lee]], etc.<ref>{{Cite web |url=https://deepmind.com/research/alphago/ |title=AlphaGo {{!}} DeepMind |website=DeepMind}}</ref> In October 2015, AlphaGo became the first [[computer Go]] program to beat a human [[professional Go player]] without [[Go handicaps|handicap]]s on a full-sized 19×19 board.<ref name="googlego">{{Cite web |url=https://googleresearch.blogspot.com/2016/01/alphago-mastering-ancient-game-of-go.html |title=Research Blog: AlphaGo: Mastering the ancient game of Go with Machine Learning |date=27 January 2016 |website=Google Research Blog}}</ref><ref name="bbcgo">{{Cite news |url=https://bbc.com/news/technology-35420579 |title=Google achieves AI 'breakthrough' by beating Go champion |date=27 January 2016 |work=[[BBC News]]}}</ref>}}

{{term|[[ambient intelligence]] (AmI)}}
{{defn|Electronic environments that are sensitive and responsive to the presence of people.}}

{{term|[[analysis of algorithms]]}}
{{defn|The determination of the [[computational complexity]] of algorithms, that is the amount of time, storage and/or other resources necessary to [[computation|execute them]]. Usually, this involves determining a [[Function (mathematics)|function]] that relates the length of an algorithm's input to the number of steps it takes (its [[time complexity]]) or the number of storage locations it uses (its [[space complexity]]).}}

{{term|[[analytics]]}}
{{defn|The discovery, interpretation, and communication of meaningful patterns in data.}}

{{term|[[answer set programming]] (ASP)}}
{{defn|A form of [[declarative programming]] oriented towards difficult (primarily [[NP-hard]]) [[search algorithm|search problem]]s. It is based on the [[stable model]] (answer set) semantics of [[logic programming]]. In ASP, search problems are reduced to computing stable models, and ''answer set solvers''—programs for generating stable models—are used to perform search.}}

{{term|[[ant colony optimization]] (ACO)}}
{{defn|A probabilistic technique for solving computational problems that can be reduced to finding good paths through [[Graph (discrete mathematics)|graphs]].}}

{{term|[[anytime algorithm]]}}
{{defn|An {{gli|algorithm}} that can return a valid solution to a problem even if it is interrupted before it ends.}}

{{term|[[application programming interface]] (API)}}
{{defn|A set of subroutine definitions, [[communication protocol]]s, and tools for building software. In general terms, it is a set of clearly defined methods of communication among various components. A good API makes it easier to develop a [[computer program]] by providing all the building blocks, which are then put together by the [[programmer]]. An API may be for a web-based system, [[operating system]], [[database system]], computer hardware, or [[software library]].}}

{{term|[[approximate string matching]]}}
{{ghat|Also '''fuzzy string searching'''.}}
{{defn|The technique of finding [[String (computing)|string]]s that match a [[pattern]] approximately (rather than exactly). The problem of approximate string matching is typically divided into two sub-problems: finding approximate [[substring]] matches inside a given string and finding dictionary strings that match the pattern approximately.}}

{{term|[[approximation error]]}}
{{defn|The discrepancy between an exact value and some approximation to it.}}

{{term|[[argumentation framework]]}}
{{ghat|Also '''argumentation system'''.}}
{{defn|A way to deal with contentious information and draw conclusions from it. In an abstract argumentation framework,<ref>See Dung (1995)</ref> entry-level information is a set of abstract arguments that, for instance, represent data or a proposition. Conflicts between arguments are represented by a [[binary relation]] on the set of arguments. In concrete terms, you represent an argumentation framework with a [[directed graph]] such that the nodes are the arguments, and the arrows represent the attack relation. There exist some extensions of the Dung's framework, like the logic-based argumentation frameworks<ref>See Besnard and Hunter (2001)</ref> or the value-based argumentation frameworks.<ref>see Bench-Capon (2002)</ref>}}

{{anchor|artificial general intelligence}}{{term|[[artificial general intelligence]] (AGI)}}
{{defn|A type of AI that matches or surpasses human cognitive capabilities across a wide range of cognitive tasks.}}

{{term|[[artificial immune system]] (AIS)}}
{{defn|A class of computationally intelligent, [[rule-based machine learning]] systems inspired by the principles and processes of the vertebrate [[immune system]]. The algorithms are typically modeled after the immune system's characteristics of [[learning]] and [[memory]] for use in [[problem-solving]].}}

{{anchor|artificial intelligence}}{{term|[[artificial intelligence]] (AI)}}
{{ghat|Also '''machine intelligence'''.}}
{{defn|Any [[intelligence]] demonstrated by [[machine]]s, in contrast to the natural intelligence<!-- boldface per WP:R#PLA --> displayed by humans and other animals. In [[computer science]], AI research is defined as the study of "{{gli|intelligent agent|intelligent agents}}": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.<ref name="Definition of AI">
Definition of AI as the study of [[intelligent agent]]s:
* {{Harvnb|Poole|Mackworth|Goebel|1998|loc=[https://people.cs.ubc.ca/~poole/ci/ch1.pdf p. 1]}}, which provides the version that is used in this article. Note that they use the term "computational intelligence" as a synonym for artificial intelligence.
* {{Harvtxt|Russell|Norvig|2003}} (who prefer the term "rational agent") and write "The whole-agent view is now widely accepted in the field" {{Harv|Russell|Norvig|2003|p=55}}.
* {{Harvnb|Nilsson|1998}}
<!-- These textbooks are the most widely used in academic AI. -->
* {{Harvnb|Legg|Hutter|2007}}.
</ref> Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other [[human mind]]s, such as "learning" and "problem solving".{{sfn|Russell|Norvig|2009|p=2}}}}

{{term|[[Artificial Intelligence Markup Language]]}}
{{defn|An [[XML]] dialect for creating [[natural language]] software agents.}}

{{term|[[Association for the Advancement of Artificial Intelligence]] (AAAI)}}
{{defn|An international, nonprofit, scientific society devoted to promote research in, and responsible use of, {{gli|artificial intelligence}}. AAAI also aims to increase public understanding of artificial intelligence (AI), improve the teaching and training of AI practitioners, and provide guidance for research planners and funders concerning the importance and potential of current AI developments and future directions.<ref>{{Cite web |url=https://aaai.org/Organization/bylaws.php |title=AAAI Corporate Bylaws}}</ref>}}

{{term|[[asymptotic computational complexity]]}}
{{defn|In {{gli|computational complexity theory}}, asymptotic computational complexity is the usage of [[asymptotic analysis]] for the estimation of computational complexity of {{gli|algorithm|algorithms}} and [[computational problem]]s, commonly associated with the usage of the [[big O notation]].}}

{{term|[[attention mechanism]]}}
{{defn|{{gli|machine learning|Machine learning}}-based '''attention''' is a mechanism mimicking [[Attention|cognitive attention]]. It calculates "soft" weights for each word, more precisely for its embedding, in the [[context window]]. It can do it either in parallel (such as in {{gli|transformer|transformers}}) or sequentially (such as in [[recursive neural network]]s). "Soft" weights can change during each runtime, in contrast to "hard" weights, which are (pre-)trained and fine-tuned and remain frozen afterwards. Multiple attention heads are used in transformer-based [[large language model]]s.}}

{{term|[[attributional calculus]]}}
{{defn|A logic and representation system defined by [[Ryszard S. Michalski]]. It combines elements of [[predicate logic]], [[propositional calculus]], and [[multi-valued logic]]. Attributional calculus provides a formal language for ''natural induction'', an inductive learning process whose results are in forms natural to people.}}

{{anchor|augmented reality}}{{term|[[augmented reality]] (AR)}}
{{Main|Augmented reality}}
{{defn|An interactive experience of a real-world environment where the objects that reside in the real-world are "augmented" by computer-generated perceptual information, sometimes across multiple sensory modalities, including [[visual]], [[Hearing|auditory]], [[haptic perception|haptic]], [[somatosensory]], and [[olfactory]].<ref name="Cipresso Giglioli Raya Riva 2011 p. ">{{cite journal | last1=Cipresso | first1=Pietro | last2=Giglioli | first2=Irene Alice Chicchi | last3=Raya | first3=iz | last4=Riva | first4=Giuseppe | title=The Past, Present, and Future of Virtual and Augmented Reality Research: A Network and Cluster Analysis of the Literature | journal=Frontiers in Psychology | volume=9 | date=2011-12-07 | pmid=30459681 | doi=10.3389/fpsyg.2018.02086 | page=2086| pmc=6232426 | doi-access=free }}</ref>}}

{{anchor|variational autoencoder}}{{term|[[autoencoder]]}}
{{defn|A type of {{gli|artificial neural network}} used to learn {{gli|feature learning|efficient codings}} of unlabeled data ({{gli|unsupervised learning}}). A common implementation is the [[variational autoencoder]] (VAE).}}

{{term|[[automata theory]]}}
{{defn|The study of [[abstract machine]]s and [[automata]], as well as the [[computational problem]]s that can be solved using them. It is a theory in [[theoretical computer science]] and [[discrete mathematics]] (a subject of study in both [[mathematics]] and [[computer science]]).}}

{{term|[[automated machine learning]] (AutoML)}}
{{defn| A field of {{gli|machine learning}} (ML) which aims to automatically configure an ML system to maximize its performance (e.g, {{gli|classification}} accuracy).}}

{{term|[[automated planning and scheduling]]}}
{{ghat|Also simply '''AI planning'''.}}
{{defn|A branch of {{gli|artificial intelligence}} that concerns the realization of [[strategies]] or action sequences, typically for execution by {{gli|intelligent agent|intelligent agents}}, [[autonomous robot]]s and [[unmanned vehicle]]s. Unlike classical [[control system|control]] and {{gli|classification}} problems, the solutions are complex and must be discovered and optimized in multidimensional space. Planning is also related to [[decision theory]].<ref>{{Citation |last1=Ghallab |first1=Malik |title=Automated Planning: Theory and Practice |url=https://laas.fr/planning/ |year=2004 |publisher=[[Morgan Kaufmann]] |isbn=978-1-55860-856-6 |last2=Nau |first2=Dana S. |last3=Traverso |first3=Paolo}}</ref>}}

{{term|[[automated reasoning]]}}
{{defn|An area of [[computer science]] and [[mathematical logic]] dedicated to understanding different aspects of [[reasoning]]. The study of automated reasoning helps produce [[computer program]]s that allow computers to reason completely, or nearly completely, automatically. Although automated reasoning is considered a sub-field of {{gli|artificial intelligence}}, it also has connections with [[theoretical computer science]], and even [[philosophy]].}}

{{anchor|autonomic computing}}{{term|[[autonomic computing]] (AC)}}
{{defn|The [[Self-management (computer science)|self-managing]] characteristics of [[distributed computing]] resources, adapting to unpredictable changes while hiding intrinsic complexity to operators and users. Initiated by [[IBM]] in 2001, this initiative ultimately aimed to develop computer systems capable of self-management, to overcome the rapidly growing complexity of computing [[systems management]], and to reduce the barrier that complexity poses to further growth.<ref name="Kephart">{{Citation |last1=Kephart |first1=J.O. |title=The vision of autonomic computing |journal=Computer |volume=36 |pages=41–52 |year=2003 |citeseerx=10.1.1.70.613 |doi=10.1109/MC.2003.1160055 |last2=Chess |first2=D.M.}}</ref>}}

{{term|[[autonomous car]]}}
{{ghat|Also '''self-driving car''', '''robot car''', and '''driverless car'''.}}
{{defn|A [[Vehicular automation|vehicle]] that is capable of sensing its environment and moving with little or no [[User interface|human input]].<ref>{{Cite conference |last1=Gehrig |first1=Stefan K. |last2=Stein |first2=Fridtjof J. |year=1999 |title=Dead reckoning and cartography using stereo vision for an automated car |conference=IEEE/RSJ International Conference on Intelligent Robots and Systems |location=Kyongju |volume=3 |pages=1507–1512 |doi=10.1109/IROS.1999.811692 |isbn=0-7803-5184-3}}</ref><ref>{{Cite news |url=https://reuters.com/article/us-autos-selfdriving-uber-idUSKBN1GV296 |title=Self-driving Uber car kills Arizona woman crossing street |date=20 March 2018 |work=Reuters}}</ref><ref name="thrun2010toward">{{Cite journal |last1=Thrun |first1=Sebastian |year=2010 |title=Toward Robotic Cars |journal=Communications of the ACM |volume=53 |issue=4 |pages=99–106 |doi=10.1145/1721654.1721679|s2cid=207177792 }}</ref>}}

{{term|[[autonomous robot]]}}
{{defn|A [[robot]] that performs [[Behavior-based robotics|behavior]]s or tasks with a high degree of [[autonomy]]. Autonomous robotics is usually considered to be a subfield of {{gli|artificial intelligence}}, [[robotics]], and [[information engineering]].<ref>{{Cite web |url=https://robots.ox.ac.uk/ |title=Information Engineering Main/Home Page |publisher=University of Oxford |access-date=2018-10-03 |archive-date=3 July 2022 |archive-url=https://web.archive.org/web/20220703164507/http://arno@robots.ox.ac.uk/ |url-status=dead }}</ref>}}
{{glossaryend}}

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==B==
{{glossary}}
{{term|[[backpropagation]]}}
{{defn|A method used in {{gli|artificial neural network|artificial neural networks}} to calculate a gradient that is needed in the calculation of the [[Artificial neural network#Components of an artificial neural network|weight]]s to be used in the network.<ref>Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron (2016) ''Deep Learning''. MIT Press. p. 196. {{ISBN|9780262035613}}</ref> Backpropagation is shorthand for "the backward propagation of errors", since an error is computed at the output and distributed backwards throughout the network's layers. It is commonly used to train {{gli|deep learning|deep neural networks}},<ref>{{Cite journal |last1=Nielsen |first1=Michael A. |year=2015 |title=Chapter 6 |url=https://neuralnetworksanddeeplearning.com/chap6.html |journal=Neural Networks and Deep Learning |access-date=5 July 2022 |archive-date=8 August 2022 |archive-url=https://web.archive.org/web/20220808100458/https://neuralnetworksanddeeplearning.com/chap6.html |url-status=dead }}</ref> a term referring to neural networks with more than one {{gli|hidden layer}}.<ref>{{Cite web |url=https://ufldl.stanford.edu/wiki/index.php/Deep_Networks:_Overview |title=Deep Networks: Overview – Ufldl |website=ufldl.stanford.edu |access-date=2017-08-04 |archive-date=16 March 2022 |archive-url=https://web.archive.org/web/20220316074444/http://ufldl.stanford.edu/wiki/index.php/Deep_Networks:_Overview |url-status=dead }}</ref>}}

{{anchor|backpropagation through structure}}{{term|[[backpropagation through structure]] (BPTS)}}
{{defn|A gradient-based technique for training {{gli|recurrent neural network|recurrent neural networks}}, proposed in a 1996 paper written by Christoph Goller and Andreas Küchler.<ref>{{cite book|first1=Christoph|last1=Goller|first2=Andreas|last2=Küchler|title=Proceedings of International Conference on Neural Networks (ICNN'96)|s2cid=6536466|chapter=Learning Task-Dependent Distributed Representations by Backpropagation Through Structure|citeseerx = 10.1.1.49.1968|year=1996|volume=1|pages=347–352|doi=10.1109/ICNN.1996.548916|isbn=0-7803-3210-5}}</ref>}}

{{anchor|backpropagation through time}}{{term|[[backpropagation through time]] (BPTT)}}
{{defn|A gradient-based technique for training certain types of {{gli|recurrent neural network|recurrent neural networks}}, such as [[Recurrent neural network#Elman networks and Jordan networks|Elman network]]s. The algorithm was independently derived by numerous researchers.<ref>{{Cite book |last1=Mozer |first1=M. C. |title=Backpropagation: Theory, architectures, and applications |publisher=Hillsdale, NJ: Lawrence Erlbaum Associates |year=1995 |editor-last=Chauvin |editor-first=Y. |pages=137–169 |language=en |chapter=A Focused Backpropagation Algorithm for Temporal Pattern Recognition |access-date=2017-08-21 |editor-last2=Rumelhart |editor-first2=D. |chapter-url=https://www.researchgate.net/publication/243781476}}</ref><ref>{{Cite tech report |title=The utility driven dynamic error propagation network |last=Robinson |first=A. J. |last2=Fallside |first2=F. |name-list-style=amp |institution=Cambridge University, Engineering Department |number=CUED/F-INFENG/TR.1 |year=1987 |url=https://bibsonomy.org/bibtex/269a88ecbac9a51cbf0b4be189c412820/idsia}}</ref><ref>{{Cite journal |last1=Werbos |first1=Paul J. |year=1988 |title=Generalization of backpropagation with application to a recurrent gas market model |url=https://zenodo.org/record/1258627 |journal=Neural Networks |volume=1 |issue=4 |pages=339–356 |doi=10.1016/0893-6080(88)90007-x}}</ref>}}

{{term|[[backward chaining]]}}
{{ghat|Also '''backward reasoning'''.}}
{{defn|An [[inference]] method described colloquially as working backward from the goal. It is used in [[automated theorem prover]]s, [[inference engine]]s, [[proof assistant]]s, and other {{gli|artificial intelligence}} applications.<ref>{{Cite book |last1=Feigenbaum |first1=Edward |url=https://archive.org/details/riseofexpertco00feig |title=The Rise of the Expert Company |publisher=Times Books |year=1988 |isbn=978-0-8129-1731-4 |page=[https://archive.org/details/riseofexpertco00feig/page/317 317] |url-access=registration}}</ref>}}

{{term|[[bag-of-words model]]}}
{{defn|A simplifying representation used in [[natural language processing]] and [[information retrieval]] (IR). In this model, a text (such as a sentence or a document) is represented as the [[multiset|bag (multiset)]] of its words, disregarding grammar and even word order but keeping [[Multiplicity (mathematics)|multiplicity]]. The bag-of-words model has also been used for {{gli|bag-of-words model in computer vision|computer vision}}.<ref name="sivic">{{Cite journal |last1=Sivic |first1=Josef |date=April 2009 |title=Efficient visual search of videos cast as text retrieval |url=https://di.ens.fr/~josef/publications/sivic09a.pdf |journal=IEEE Transactions on Pattern Analysis and Machine Intelligence |volume=31 |issue=4 |pages=591–605 |citeseerx=10.1.1.174.6841 |doi=10.1109/TPAMI.2008.111 |pmid=19229077 |s2cid=9899337 |access-date=5 July 2022 |archive-date=31 March 2022 |archive-url=https://web.archive.org/web/20220331011840/https://www.di.ens.fr/~josef/publications/sivic09a.pdf |url-status=dead }}</ref> The bag-of-words model is commonly used in methods of [[document classification]] where the (frequency of) occurrence of each word is used as a {{gli|feature}} for training a {{gli|classification|classifier}}.<ref>McTear et al 2016, p. 167.</ref>}}

{{term|[[bag-of-words model in computer vision]]}}
{{defn|In computer vision, the bag-of-words model (BoW model) can be applied to [[Computer vision|image classification]], by treating {{gli|feature|image features}} as words. In document classification, a [[bag of words]] is a [[sparse vector]] of occurrence counts of words; that is, a sparse [[histogram]] over the vocabulary. In [[computer vision]], a ''bag of visual words'' is a vector of occurrence counts of a vocabulary of local image features.}}

{{term|[[batch normalization]]}}
{{defn|A technique for improving the performance and stability of {{gli|artificial neural network|artificial neural networks}}. It is a technique to provide any layer in a neural network with inputs that are zero mean/unit variance.<ref>{{Cite web |url=https://kratzert.github.io/2016/02/12/understanding-the-gradient-flow-through-the-batch-normalization-layer.html |title=Understanding the backward pass through Batch Normalization Layer |website=kratzert.github.io |access-date=24 April 2018}}</ref> Batch normalization was introduced in a 2015 paper.<ref>{{Cite arXiv |last1=Ioffe |first1=Sergey |last2=Szegedy |first2=Christian |year=2015 |title=Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift |class=cs.LG |eprint=1502.03167}}</ref><ref>{{Cite web |url=https://medium.com/deeper-learning/glossary-of-deep-learning-batch-normalisation-8266dcd2fa82 |title=Glossary of Deep Learning: Batch Normalisation |date=2017-06-27 |website=medium.com |access-date=24 April 2018}}</ref> It is used to normalize the input layer by adjusting and scaling the activations.}}

{{term|[[Bayesian programming]]}}
{{defn|A formalism and a methodology for having a technique to specify [[Probability distribution|probabilistic model]]s and solve problems when less than the necessary information is available.}}

{{term|[[bees algorithm]]}}
{{defn|A population-based {{gli|search algorithm}} which was developed by Pham, Ghanbarzadeh and et al. in 2005.<ref name="Pham & al, 2005">Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S and Zaidi M. The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005.</ref> It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighborhood search combined with global search, and can be used for both [[combinatorial optimization]] and [[continuous optimization]]. The only condition for the application of the bees algorithm is that some measure of distance between the solutions is defined. The effectiveness and specific abilities of the bees algorithm have been proven in a number of studies.<ref name="Pham & Castellani, 2009">Pham, D.T., Castellani, M. (2009), [https://pic.sagepub.com/content/223/12/2919.short The Bees Algorithm – Modelling Foraging Behaviour to Solve Continuous Optimisation Problems] {{Webarchive|url=https://web.archive.org/web/20161109125453/https://pic.sagepub.com/content/223/12/2919.short |date=9 November 2016 }}. Proc. ImechE, Part C, 223(12), 2919–2938.</ref><ref>{{Cite journal |last1=Pham |first1=D. T. |last2=Castellani |first2=M. |year=2014 |title=Benchmarking and comparison of nature-inspired population-based continuous optimisation algorithms |journal=Soft Computing |volume=18 |issue=5 |pages=871–903 |doi=10.1007/s00500-013-1104-9|s2cid=35138140 }}</ref><ref>{{Cite journal |last1=Pham |first1=Duc Truong |last2=Castellani |first2=Marco |year=2015 |title=A comparative study of the Bees Algorithm as a tool for function optimisation |journal=Cogent Engineering |volume=2 |doi=10.1080/23311916.2015.1091540 |doi-access=free}}</ref><ref name="Nasrinpour & Massah & Teshnehlab 2017">{{Cite journal |last1=Nasrinpour |first1=H. R. |last2=Massah Bavani |first2=A. |last3=Teshnehlab |first3=M. |year=2017 |title=Grouped Bees Algorithm: A Grouped Version of the Bees Algorithm |journal=Computers |volume=6 |issue=1 |page=5 |doi=10.3390/computers6010005 |doi-access=free}}</ref>}}

{{anchor|behavior informatics}}{{term|[[behavior informatics]] (BI)}}
{{defn|The informatics of behaviors so as to obtain behavior intelligence and behavior insights.<ref>{{Cite journal |last1=Cao |first1=Longbing |year=2010 |title=In-depth Behavior Understanding and Use: the Behavior Informatics Approach |journal=Information Science |volume=180 |issue=17 |pages=3067–3085 |doi=10.1016/j.ins.2010.03.025|arxiv=2007.15516 |s2cid=7400761 }}</ref>}}

{{term|[[behavior tree (artificial intelligence, robotics and control)|behavior tree]] (BT)}}
{{defn|A [[mathematical model]] of [[automated planning and scheduling|plan]] execution used in [[computer science]], [[robotics]], [[control system]]s and [[artificial intelligence (video games)|video game]]s. They describe switchings between a finite set of tasks in a modular fashion. Their strength comes from their ability to create very complex tasks composed of simple tasks, without worrying how the simple tasks are implemented. BTs present some similarities to [[State machine|hierarchical state machine]]s with the key difference that the main building block of a behavior is a task rather than a state. Its ease of human understanding make BTs less error-prone and very popular in the game developer community. BTs have shown to generalize several other control architectures.<ref name="Colledanchise TRO16">Colledanchise Michele, and Ögren Petter 2016. [https://michelecolledanchise.com/tro16colledanchise.pdf How Behavior Trees Modularize Hybrid Control Systems and Generalize Sequential Behavior Compositions, the Subsumption Architecture, and Decision Trees. In IEEE Transactions on Robotics vol.PP, no.99, pp.1–18 (2016)]</ref><ref name="Colledanchise BOOK17">{{Cite book|arxiv = 1709.00084|doi = 10.1201/9780429489105|title = Behavior Trees in Robotics and AI|year = 2018|last1 = Colledanchise|first1 = Michele|last2 = Ögren|first2 = Petter|isbn = 9780429950902|s2cid = 27470659}}</ref>}}

{{term|[[belief–desire–intention software model]] (BDI)}}
{{defn|A software model developed for programming {{gli|intelligent agent|intelligent agents}}. Superficially characterized by the implementation of an agent's ''beliefs'', ''desires'' and ''intentions'', it actually uses these concepts to solve a particular problem in agent programming. In essence, it provides a mechanism for separating the activity of selecting a plan (from a plan library or an external planner application) from the execution of currently active plans. Consequently, BDI agents are able to balance the time spent on deliberating about plans (choosing what to do) and executing those plans (doing it). A third activity, creating the plans in the first place (planning), is not within the scope of the model, and is left to the system designer and programmer.}}

{{term|[[bias–variance tradeoff]]}}
{{defn|In [[statistics]] and {{gli|machine learning}}, the bias–variance tradeoff is the property of a set of predictive models whereby models with a lower [[Bias of an estimator|bias]] in [[statistical parameter|parameter]] [[estimation theory|estimation]] have a higher [[variance]] of the parameter estimates across [[sample (statistics)|samples]], and vice versa.}}

{{term|[[big data]]}}
{{defn|A term used to refer to [[data set]]s that are too large or complex for traditional [[data processing|data-processing]] [[application software]] to adequately deal with. Data with many cases (rows) offer greater [[statistical power]], while data with higher complexity (more attributes or columns) may lead to a higher [[false discovery rate]].<ref>{{Cite journal |last1=Breur |first1=Tom |date=July 2016 |title=Statistical Power Analysis and the contemporary "crisis" in social sciences |journal=Journal of Marketing Analytics  |volume=4 |issue=2–3 |pages=61–65 |doi=10.1057/s41270-016-0001-3 |issn=2050-3318 |doi-access=free}}</ref>}}

{{term|[[Big O notation]]}}
{{defn|A mathematical notation that describes the [[asymptotic analysis|limiting behavior]] of a [[function (mathematics)|function]] when the [[Argument of a function|argument]] tends towards a particular value or infinity. It is a member of a family of notations invented by [[Paul Bachmann]],<ref name="Bachmann">{{Cite book |last1=Bachmann |first1=Paul |url=https://archive.org/stream/dieanalytischeza00bachuoft#page/402/mode/2up |title=Analytische Zahlentheorie |date=1894 |publisher=Teubner |volume=2 |location=Leipzig |language=de |trans-title=Analytic Number Theory |author-link=Paul Bachmann}}</ref> [[Edmund Landau]],<ref name="Landau">{{Cite book |last1=Landau |first1=Edmund |url=https://archive.org/details/handbuchderlehre01landuoft |title=Handbuch der Lehre von der Verteilung der Primzahlen |date=1909 |publisher=B. G. Teubner |location=Leipzig |page=883 |language=de |trans-title=Handbook on the theory of the distribution of the primes |author-link=Edmund Landau}}</ref> and others, collectively called Bachmann–Landau notation or asymptotic notation.}}

{{term|[[binary tree]]}}
{{defn|A [[tree structure|tree]] [[data structure]] in which each node has at most two [[child node|children]], which are referred to as the ''{{visible anchor|left child}}'' and the ''{{visible anchor|right child}}''. A [[recursive definition]] using just [[set theory]] notions is that a (non-empty) binary tree is a [[tuple]] (''L'', ''S'', ''R''), where ''L'' and ''R'' are binary trees or the [[empty set]] and ''S'' is a [[singleton set]].<ref name="GarnierTaylor2009">{{Cite book |editor-last=Garnier |editor-first=Rowan |url=https://books.google.com/books?id=WnkZSSc4IkoC&pg=PA620 |title=Discrete Mathematics: Proofs, Structures and Applications, Third Edition |last1=John |first1=Taylor |publisher=CRC Press |year=2009 |isbn=978-1-4398-1280-8 |page=620}}</ref> Some authors allow the binary tree to be the empty set as well.<ref name="Skiena2009">{{Cite book |last1=Skiena |first1=Steven S |url=https://books.google.com/books?id=7XUSn0IKQEgC&pg=PA77 |title=The Algorithm Design Manual |publisher=Springer Science & Business Media |year=2009 |isbn=978-1-84800-070-4 |page=77}}</ref>}}

{{term|[[blackboard system]]}}
{{defn|An {{gli|artificial intelligence}} approach based on the [[blackboard architectural model]],<ref>{{Cite journal |last1=Erman |first1=L. D. |last2=Hayes-Roth |first2=F. |last3=Lesser |first3=V. R. |last4=Reddy |first4=D. R. |year=1980 |title=The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty |journal=ACM Computing Surveys |volume=12 |issue=2 |pages=213 |doi=10.1145/356810.356816|s2cid=118556 }} <!-- Erman, Hayes-Roth, & Reddy (1980). "The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty" --></ref><ref>{{Cite journal |last1=Corkill |first1=Daniel D. |date=September 1991 |title=Blackboard Systems |url=https://bbtech.com/papers/ai-expert.pdf |journal=AI Expert |volume=6 |issue=9 |pages=40–47 |access-date=5 July 2022 |archive-date=16 April 2012 |archive-url=https://web.archive.org/web/20120416034609/http://www.bbtech.com/papers/ai-expert.pdf |url-status=dead }}</ref><ref>* {{Cite tech report |first=H. Yenny |last=Nii |title=Blackboard Systems |number=STAN-CS-86-1123 |institution=Department of Computer Science, Stanford University |year=1986 |url=https://i.stanford.edu/pub/cstr/reports/cs/tr/86/1123/CS-TR-86-1123.pdf |access-date=2013-04-12}}</ref><ref>{{Cite journal |last1=Hayes-Roth |first1=B.|author1-link=Barbara Hayes-Roth |year=1985 |title=A blackboard architecture for control |journal=Artificial Intelligence |volume=26 |issue=3 |pages=251–321 |doi=10.1016/0004-3702(85)90063-3}} <!-- Hayes-Roth (1985). "A blackboard architecture for control" --></ref> where a common knowledge base, the "blackboard", is iteratively updated by a diverse group of specialist knowledge sources, starting with a problem specification and ending with a solution. Each knowledge source updates the blackboard with a partial solution when its internal constraints match the blackboard state. In this way, the specialists work together to solve the problem.}}

{{term|[[Boltzmann machine]]}}
{{ghat|Also '''stochastic Hopfield network with hidden units'''.}}
{{defn|A type of [[stochastic neural network|stochastic]] [[recurrent neural network]] and [[Markov random field]].<ref>{{Cite journal |last1=Hinton |first1=Geoffrey E. |date=2007-05-24 |title=Boltzmann machine |journal=Scholarpedia  |volume=2 |issue=5 |pages=1668 |bibcode=2007SchpJ...2.1668H |doi=10.4249/scholarpedia.1668 |issn=1941-6016 |doi-access=free}}</ref>  Boltzmann machines can be seen as the [[stochastic process|stochastic]], [[generative model|generative]] counterpart of [[Hopfield network]]s.}}

{{term|[[Boolean satisfiability problem]]}}
{{ghat|Also '''propositional satisfiability problem'''; abbreviated '''SATISFIABILITY''' or '''SAT'''.}}
{{defn|The problem of determining if there exists an [[Interpretation (logic)|interpretation]] that [[Satisfiability|satisfies]] a given [[Boolean logic|Boolean]] [[Formula (mathematical logic)|formula]]. In other words, it asks whether the variables of a given Boolean formula can be consistently replaced by the values TRUE or FALSE in such a way that the formula evaluates to TRUE. If this is the case, the formula is called ''satisfiable''. On the other hand, if no such assignment exists, the function expressed by the formula is [[Contradiction#Contradiction in formal logic|FALSE]] for all possible variable assignments and the formula is ''unsatisfiable''. For example, the formula "''a'' AND NOT ''b''" is satisfiable because one can find the values ''a''&nbsp;{{=}}&nbsp;TRUE and ''b''&nbsp;{{=}}&nbsp;FALSE, which make (''a'' AND NOT ''b'')&nbsp;{{=}}&nbsp;TRUE. In contrast, "''a'' AND NOT ''a''" is unsatisfiable.}}

{{anchor|boosting}}{{term|[[Boosting (machine learning)|boosting]]}}
{{defn|A {{gli|machine learning}} {{gli|ensemble learning|ensemble}} [[metaheuristic]] for primarily reducing {{gli|bias–variance tradeoff|bias (as opposed to variance)}}, by training models sequentially, each one correcting the errors of its predecessor.}}

{{anchor|bagging}}{{anchor|bootstrapping}}{{term|[[bootstrap aggregating]]}}
{{ghat|Also '''bagging''' or '''bootstrapping'''.}}
{{defn|A {{gli|machine learning}} {{gli|ensemble learning|ensemble}} [[metaheuristic]] for primarily reducing {{gli|bias–variance tradeoff|variance (as opposed to bias)}}, by training multiple models independently and averaging their predictions.}}

{{term|[[brain technology]]}}
{{ghat|Also '''self-learning know-how system'''.}}
{{defn|A technology that employs the latest findings in [[neuroscience]]. The term was first introduced by the Artificial Intelligence Laboratory in [[Zurich, Switzerland]], in the context of the [[ROBOY]] project.<ref>[https://nzz.ch/aktuell/zuerich/uebersicht/die-zangengeburt-eines-designierten-stammvaters-1.18029566 ''NZZ- Die Zangengeburt eines möglichen Stammvaters'']. Website [[Neue Zürcher Zeitung]]. Seen 16. August 2013.</ref> Brain Technology can be employed in robots,<ref>[https://roboy.org/mediaundnews.html ''Official Homepage Roboy''] {{Webarchive|url=https://web.archive.org/web/20130803052035/https://roboy.org/mediaundnews.html |date=2013-08-03 }}. Website Roboy. Seen 16. August 2013.</ref> [[Starmind International|know-how management system]]s<ref>[https://starmind.com/ ''Official Homepage Starmind'']. Website Starmind. Seen 16. August 2013.</ref> and any other application with self-learning capabilities. In particular, Brain Technology applications allow the visualization of the underlying learning architecture often coined as "know-how maps".}}

{{term|[[branching factor]]}}
{{defn|In [[computing]], [[tree data structure]]s, and [[game theory]], the number of [[child node|children]] at each {{gli|node}}, the [[outdegree]]. If this value is not uniform, an ''average branching factor'' can be calculated.}}

{{term|[[brute-force search]]}}
{{ghat|Also '''exhaustive search''' or '''generate and test'''.}}
{{defn|A very general [[problem-solving]] technique and [[algorithmic paradigm]] that consists of systematically enumerating all possible candidates for the solution and checking whether each candidate satisfies the problem's statement.}}
{{glossaryend}}

{{Compact TOC|side=yes|center=yes|top=yes|num=yes|extlinks=yes|seealso=yes|refs=yes|nobreak=yes|}}

==C==
{{glossary}}
{{term|[[capsule neural network]] (CapsNet)}}
{{defn|A {{gli|machine learning}} system that is a type of {{gli|artificial neural network}} (ANN) that can be used to better model hierarchical relationships. The approach is an attempt to more closely mimic biological neural organization.<ref>{{Cite arXiv |eprint=1710.09829 |class=cs.CV |first1=Sara |last1=Sabour |first2=Nicholas |last2=Frosst |title=Dynamic Routing Between Capsules |date=2017-10-26 |last3=Hinton |first3=Geoffrey E.}}</ref>}}

{{anchor|case-based reasoning}}{{term|[[case-based reasoning]] (CBR)}}
{{defn|Broadly construed, the process of solving new problems based on the solutions of similar past problems.}}

{{term|[[chatbot]]}}
{{ghat|Also '''smartbot''', '''talkbot''', '''chatterbot''', '''bot''', '''IM bot''', '''interactive agent''', '''conversational interface''', or '''artificial conversational entity'''.}}
{{defn|A [[computer program]] or an {{gli|artificial intelligence}} which conducts a [[conversation]] via auditory or textual methods.<ref name="target">{{Cite web |url=https://searchdomino.techtarget.com/sDefinition/0,,sid4_gci935566,00.html |title=What is a chatbot? |website=techtarget.com |access-date=30 January 2017}}</ref>}}

{{term|[[cloud robotics]]}}
{{defn|A field of [[robotics]] that attempts to invoke cloud technologies such as [[cloud computing]], [[cloud storage]], and other [[Internet technologies]] centred on the benefits of converged infrastructure and shared services for robotics. When connected to the cloud, robots can benefit from the powerful computation, storage, and communication resources of modern [[data center]] in the cloud, which can process and share information from various robots or agent (other machines, smart objects, humans, etc.). Humans can also delegate tasks to robots remotely through [[network (computing)|network]]s. Cloud computing technologies enable robot systems to be endowed with powerful capability whilst reducing costs through cloud technologies. Thus, it is possible to build lightweight, low cost, smarter robots have intelligent "brain" in the cloud. The "brain" consists of [[data center]], [[knowledge base]], task planners, {{gli|deep learning}}, information processing, environment models, communication support, etc.<ref name="RASrobot">{{Cite journal |last1=Civera |first1=Javier |last2=Ciocarlie |first2=Matei |last3=Aydemir |first3=Alper |last4=Bekris |first4=Kostas |last5=Sarma |first5=Sanjay |s2cid=16080778 |year=2015 |title=Guest Editorial Special Issue on Cloud Robotics and Automation |journal=IEEE Transactions on Automation Science and Engineering |volume=12 |issue=2 |pages=396–397 |doi=10.1109/TASE.2015.2409511|doi-access=free }}</ref><ref>{{Cite web |url=https://roboearth.org/ |title=Robo Earth - Tech News |website=Robo Earth}}</ref><ref name="kengoldberg">{{Cite web |url=https://goldberg.berkeley.edu/cloud-robotics |title=Cloud Robotics and Automation |last1=Goldberg |first1=Ken}}</ref><ref name="sites.google.com">{{Cite web |url=https://sites.google.com/site/ruijiaoli/blogs/page |title=Cloud Robotics-Enable cloud computing for robots |last1=Li |first1=R |access-date=7 December 2014}}</ref>}}

{{term|[[cluster analysis]]}}
{{ghat|Also '''clustering'''.}}
{{defn|The task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is a main task of exploratory [[data mining]], and a common technique for [[statistical]] [[data analysis]], used in many fields, including {{gli|machine learning}}, [[pattern recognition]], [[image analysis]], [[information retrieval]], [[bioinformatics]], [[data compression]], and [[computer graphics]].}}

{{term|[[Cobweb (clustering)|Cobweb]]}}
{{defn|An incremental system for hierarchical [[conceptual clustering]]. COBWEB was invented by Professor [[Douglas H. Fisher]], currently at Vanderbilt University.<ref>{{Cite journal |last1=Fisher |first1=Douglas |year=1987 |title=Knowledge acquisition via incremental conceptual clustering |journal=Machine Learning |volume=2 |issue=2 |pages=139–172 |doi=10.1007/BF00114265 |doi-access=free}}</ref><ref>{{Cite conference |last1=Fisher |first1=Douglas H. |date=July 1987 |title=Improving inference through conceptual clustering |conference=AAAI Conference |location=Seattle Washington |pages=461–465 |book-title=Proceedings of the 1987 AAAI Conferences}}</ref> COBWEB incrementally organizes observations into a [[classification tree]]. Each node in a classification tree represents a class (concept) and is labeled by a probabilistic concept that summarizes the attribute-value distributions of objects classified under the node. This classification tree can be used to predict missing attributes or the class of a new object.<ref>{{Cite book |last1=Iba |first1=William |last2=Langley |first2=Pat |title=Formal approaches in categorization |date=2011-01-27 |publisher=Cambridge University Press |isbn=9780521190480 |editor-last=Pothos |editor-first=Emmanuel M. |editor-last2=Wills |editor-first2=Andy J. |location=Cambridge |pages=253–273 |chapter=Cobweb models of categorization and probabilistic concept formation}}</ref>}}

{{term|[[cognitive architecture]]}}
{{defn|The [[Institute of Creative Technologies]] defines cognitive architecture as: "hypothesis about the fixed structures that provide a mind, whether in natural or artificial systems, and how they work together – in conjunction with knowledge and skills embodied within the architecture – to yield intelligent behavior in a diversity of complex environments."<ref>Refer to the ICT website: https://cogarch.ict.usc.edu/</ref>}}

{{term|[[cognitive computing]]}}
{{defn|In general, the term cognitive computing has been used to refer to new hardware and/or software that [[Neuromorphic computing|mimics the functioning]] of the [[human brain]]<ref>{{Cite web |url=https://labs.hpe.com/research/next-next/brain/ |title=Hewlett Packard Labs |access-date=5 July 2022 |archive-date=30 October 2016 |archive-url=https://web.archive.org/web/20161030143900/http://www.labs.hpe.com/research/next-next/brain/ |url-status=dead }}</ref><ref>Terdiman, Daniel (2014) .IBM's TrueNorth processor mimics the human brain.https://cnet.com/news/ibms-truenorth-processor-mimics-the-human-brain/</ref><ref>Knight, Shawn (2011). ''[https://techspot.com/news/45138-ibm-unveils-cognitive-computing-chips-that-mimic-human-brain.html IBM unveils cognitive computing chips that mimic human brain]'' TechSpot: August 18, 2011, 12:00 PM</ref><ref>Hamill, Jasper (2013). ''[https://theregister.co.uk/2013/08/08/ibm_unveils_computer_architecture_based_upon_your_brain/ Cognitive computing: IBM unveils software for its brain-like SyNAPSE chips]'' The Register: August 8, 2013</ref><ref name="Denning">{{Cite journal |last1=Denning. |first1=P.J. |year=2014 |title=Surfing Toward the Future |journal=Communications of the ACM |volume=57 |issue=3 |pages=26–29 |doi=10.1145/2566967|s2cid=20681733 }}</ref><ref>{{Cite thesis |last1=Ludwig |first1=Lars |year=2013 |title=Extended Artificial Memory: Toward an integral cognitive theory of memory and technology |url=https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/3662 |format=pdf |publisher=Technical University of Kaiserslautern |access-date=2017-02-07}}</ref> and helps to improve human decision-making.<ref>{{Cite web |url=https://hpl.hp.com/research/ |title=Research at HP Labs |access-date=5 July 2022 |archive-date=7 March 2022 |archive-url=https://web.archive.org/web/20220307214522/https://www.hpl.hp.com/research/ |url-status=dead }}</ref> In this sense, CC is a new type of computing with the goal of more accurate models of how the human brain/[[mind]] senses, [[reason]]s, and responds to stimulus.}}

{{term|[[cognitive science]]}}
{{defn|The interdisciplinary scientific study of the [[mind]] and its processes.<ref>Cognitive science is an interdisciplinary field of researchers from Linguistics, psychology, neuroscience, philosophy, computer science, and anthropology that seek to understand the mind. [https://aft.org/newspubs/periodicals/ae/summer2002/willingham.cfm How We Learn: Ask the Cognitive Scientist]</ref>}}

{{term|[[combinatorial optimization]]}}
{{defn|In [[Operations Research]], [[applied mathematics]] and [[theoretical computer science]], combinatorial optimization <!-- synonymous or subfield?: discrete optimization{{Citation needed|date=May 2012}} --> is a topic that consists of finding an optimal object from a [[finite set]] of objects.<ref>Schrijver, Alexander (February 1, 2006). A Course in Combinatorial Optimization (PDF), page 1.</ref>}}

{{term|[[committee machine]]}}
{{defn|A type of {{gli|artificial neural network}} using a [[divide and conquer]] strategy in which the responses of multiple neural networks (experts) are combined into a single response.<ref>HAYKIN, S. Neural Networks – A Comprehensive Foundation. Second edition. Pearson Prentice Hall: 1999.</ref>  The combined response of the committee machine is supposed to be superior to those of its constituent experts. Compare [[ensembles of classifiers]].}}

{{term|[[commonsense knowledge]]}}
{{defn|In {{gli|artificial intelligence}} research, commonsense knowledge consists of facts about the everyday world, such as "Lemons are sour", that all humans are expected to know. The first AI program to address common sense knowledge was [[Advice Taker]] in 1959 by John McCarthy.<ref>{{Cite web |url=https://www-formal.stanford.edu/jmc/mcc59/mcc59.html |title=PROGRAMS WITH COMMON SENSE |website=www-formal.stanford.edu |access-date=2018-04-11}}</ref>}}

{{term|[[commonsense reasoning]]}}
{{defn|A branch of artificial intelligence concerned with simulating the human ability to make presumptions about the type and essence of ordinary situations they encounter every day.<ref>{{Cite magazine |last1=Davis |first1=Ernest |last2=Marcus |first2=Gary |year=2015 |title=Commonsense reasoning |url=https://cacm.acm.org/magazines/2015/9/191169-commonsense-reasoning-and-commonsense-knowledge-in-artificial-intelligence/fulltext |magazine=Communications of the ACM |volume=58 |pages=92–103 |doi=10.1145/2701413 |number=9}}</ref>}}

{{term|[[computational chemistry]]}}
{{defn|A branch of [[chemistry]] that uses [[computer simulation]] to assist in solving chemical problems.}}

{{term|[[computational complexity theory]]}}
{{defn|Focuses on classifying computational problems according to their inherent difficulty, and relating these classes to each other. A computational problem is a task solved by a computer. A computation problem is solvable by mechanical application of mathematical steps, such as an algorithm.}}

{{term|[[computational creativity]]}}
{{ghat|Also '''artificial creativity''', '''mechanical creativity''', '''creative computing''', or '''creative computation'''.}}
{{defn|A multidisciplinary endeavour that includes the fields of {{gli|artificial intelligence}}, [[cognitive psychology]], [[philosophy]], and [[the arts]].}}

{{term|[[computational cybernetics]]}}
{{defn|The integration of [[cybernetics]] and {{gli|computational intelligence}} techniques.}}

{{term|[[computational humor]]}}
{{defn|A branch of [[computational linguistics]] and {{gli|artificial intelligence}} which uses [[computer]]s in [[humor research]].<ref>Hulstijn, J, and Nijholt, A. (eds.). Proceedings of the International Workshop on Computational Humor. Number 12 in Twente Workshops on Language Technology, Enschede, Netherlands. University of Twente, 1996.</ref>}}

{{anchor|computational intelligence}}{{term|[[computational intelligence]] (CI)}}
{{defn|Usually refers to the ability of a [[computer]] to learn a specific task from data or experimental observation.}}

{{term|[[computational learning theory]]}}
{{defn|In [[computer science]], computational learning theory (or just learning theory) is a subfield of {{gli|artificial intelligence}} devoted to studying the design and analysis of {{gli|machine learning}} algorithms.<ref name="ACL">{{Cite web |url=https://learningtheory.org/ |title=ACL – Association for Computational Learning}}</ref>}}

{{term|[[computational linguistics]]}}
{{defn|An [[interdisciplinary]] field concerned with the statistical or rule-based modeling of [[natural language]] from a computational perspective, as well as the study of appropriate computational approaches to linguistic questions.}}

{{term|[[computational mathematics]]}}
{{defn|The mathematical research in areas of science where [[computation|computing]] plays an essential role.}}

{{term|[[computational neuroscience]]}}
{{ghat|Also '''theoretical neuroscience''' or '''mathematical neuroscience'''.}} 
{{defn|A branch of [[neuroscience]] which employs mathematical models, theoretical analysis and abstractions of the brain to understand the principles that govern the [[Developmental neuroscience|development]], [[Neuroanatomy|structure]], [[Neurophysiology|physiology]], and [[Cognitive neuroscience|cognitive abilities]] of the [[nervous system]].<ref>Trappenberg, Thomas P. (2002). Fundamentals of Computational Neuroscience. United States: Oxford University Press Inc. p. 1. {{ISBN|978-0-19-851582-1}}.</ref><ref>What is computational neuroscience? Patricia S. Churchland, Christof Koch, Terrence J. Sejnowski. in Computational Neuroscience pp.46–55. Edited by Eric L. Schwartz. 1993. MIT Press {{Cite web |title=Computational Neuroscience Edited by Eric L. Schwartz |url=https://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=7195 |url-status=dead |archive-url=https://web.archive.org/web/20110604124206/https://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=7195 |archive-date=2011-06-04 |access-date=2009-06-11}}</ref><ref>{{Cite web |url=https://mitpress.mit.edu/books/theoretical-neuroscience |title=Theoretical Neuroscience |website=The MIT Press  |url-status=dead |archive-url=https://web.archive.org/web/20180531150713/https://mitpress.mit.edu/books/theoretical-neuroscience |archive-date=31 May 2018 |access-date=2018-05-24}}</ref><ref>{{Cite book |last1=Gerstner |first1=W. |title=Neuronal Dynamics |last2=Kistler, W. |last3=Naud, R. |last4=Paninski, L. |publisher=Cambridge University Press |year=2014 |isbn=9781107447615 |location=Cambridge, UK}}</ref>}}

{{term|[[computational number theory]]}}
{{ghat|Also '''algorithmic number theory'''.}}
{{defn|The study of {{gli|algorithm|algorithms}} for performing [[number theoretic]] [[computation]]s.}}

{{term|[[computational problem]]}}
{{defn|In [[theoretical computer science]], a computational problem is a [[mathematical object]] representing a collection of questions that [[computer]]s might be able to solve.}}

{{term|[[computational statistics]]}}
{{ghat|Also '''statistical computing'''.}}
{{defn|The interface between [[statistics]] and {{gli|computer science}}.}}

{{anchor|computer-automated design}}{{term|[[computer-automated design]] (CAutoD)}}
{{defn|Design automation usually refers to [[electronic design automation]], or [[Design Automation]] which is a [[Product Configurator]]. Extending [[Computer-Aided Design]] (CAD), automated design and computer-automated design<ref name="IBM">{{Cite journal |last1=Kamentsky |first1=L.A. |last2=Liu |first2=C.-N. |year=1963 |title=Computer-Automated Design of Multifont Print Recognition Logic |url=https://domino.research.ibm.com/tchjr/journalindex.nsf/0/a5cb0910ea78194885256bfa00683e5a?OpenDocument |journal=IBM Journal of Research and Development |volume=7 |issue=1 |page=2 |doi=10.1147/rd.71.0002 |access-date=5 July 2022 |archive-date=3 March 2016 |archive-url=https://web.archive.org/web/20160303202147/http://domino.research.ibm.com/tchjr/journalindex.nsf/0/a5cb0910ea78194885256bfa00683e5a?OpenDocument |url-status=dead }}</ref><ref>{{Cite journal |last1=Brncick |first1=M |year=2000 |title=Computer automated design and computer automated manufacture |journal=Phys Med Rehabil Clin N Am |volume=11 |issue=3 |pages=701–13 |doi=10.1016/s1047-9651(18)30806-4 |pmid=10989487}}</ref><ref>{{Cite journal |last1=Li |first1=Y. |display-authors=etal |year=2004 |title=CAutoCSD - Evolutionary search and optimisation enabled computer automated control system design |url=https://link.springer.com/article/10.1007%2Fs11633-004-0076-8 |journal=International Journal of Automation and Computing |volume=1 |issue=1 |pages=76–88 |doi=10.1007/s11633-004-0076-8|s2cid=55417415 }}</ref> are concerned with a broader range of applications, such as [[automotive engineering]], [[civil engineering]],<ref>{{Cite journal |last1=Kramer |first1=GJE |last2=Grierson |first2=DE |title=Computer automated design of structures under dynamic loads |journal=Computers & Structures |year=1989 |volume=32 |issue=2 |pages=313–325 |doi=10.1016/0045-7949(89)90043-6}}</ref><ref>{{Cite journal |last1=Moharrami |first1=H |last2=Grierson |first2=DE |title=Computer-Automated Design of Reinforced Concrete Frameworks |journal=Journal of Structural Engineering |year=1993 |volume=119 |issue=7 |pages=2036–2058 |doi=10.1061/(asce)0733-9445(1993)119:7(2036)}}</ref><ref>{{Cite journal |last1=Xu |first1=L |last2=Grierson |first2=DE |title=Computer-Automated Design of Semirigid Steel Frameworks |journal=Journal of Structural Engineering |year=1993 |volume=119 |issue=6 |pages=1740–1760 |doi=10.1061/(asce)0733-9445(1993)119:6(1740)}}</ref><ref>Barsan, GM; Dinsoreanu, M, (1997). Computer-automated design based on structural performance criteria, Mouchel Centenary Conference on Innovation in Civil and Structural Engineering, Aug 19-21, Cambridge England, Innovation in Civil and Structural Engineering, 167–172</ref> [[composite material]] design, [[control engineering]],<ref>{{Cite journal |last1=Li |first1=Yun |year=1996 |title=Genetic algorithm automated approach to the design of sliding mode control systems |journal=International Journal of Control |volume=63 |issue=4 |pages=721–739 |doi=10.1080/00207179608921865}}</ref> dynamic [[system identification]] and optimization,<ref>{{Cite journal |last1=Li |first1=Yun |last2=Chwee Kim |first2=Ng |last3=Chen Kay |first3=Tan |year=1995 |title=Automation of Linear and Nonlinear Control Systems Design by Evolutionary Computation |url=https://sciencedirect.com/science/article/pii/S1474667017451585/pdf?md5=b7aedf998282848dfcf44a1ea2f003dd&pid=1-s2.0-S1474667017451585-main.pdf |journal=IFAC Proceedings Volumes |volume=28 |issue=16 |pages=85–90 |doi=10.1016/S1474-6670(17)45158-5}}</ref> [[financial]] systems, industrial equipment, {{gli|mechatronics|mechatronic}} systems, [[steel construction]],<ref>Barsan, GM, (1995) Computer-automated design of semirigid steel frameworks according to EUROCODE-3, Nordic Steel Construction Conference 95, JUN 19–21, 787–794</ref> structural [[optimisation]],<ref>{{Cite journal |last1=Gray |first1=Gary J. |last2=Murray-Smith |first2=David J. |last3=Li |first3=Yun |display-authors=etal |year=1998 |title=Nonlinear model structure identification using genetic programming |url=https://sciencedirect.com/science/article/pii/S0967066198000872/pdf?md5=5ad89d3029a3ebad83086271f3c78f75&pid=1-s2.0-S0967066198000872-main.pdf |journal=Control Engineering Practice |volume=6 |issue=11 |pages=1341–1352 |doi=10.1016/s0967-0661(98)00087-2}}</ref> and the invention of novel systems. More recently, traditional CAD simulation is seen to be transformed to CAutoD by biologically inspired {{gli|machine learning}},<ref>{{cite journal | url=https://ieeexplore.ieee.org/document/6052374 | doi=10.1109/MCI.2011.942584 | title=Evolutionary Computation Meets Machine Learning: A Survey | year=2011 | last1=Zhang | first1=Jun | last2=Zhan | first2=Zhi-hui | last3=Lin | first3=Ying | last4=Chen | first4=Ni | last5=Gong | first5=Yue-Jiao | last6=Zhong | first6=Jing-hui | last7=Chung | first7=Henry S.H. | last8=Li | first8=Yun | last9=Shi | first9=Yu-hui | journal=IEEE Computational Intelligence Magazine | volume=6 | issue=4 | pages=68–75 | s2cid=6760276 }}</ref> including heuristic [[search algorithm|search technique]]s such as [[evolutionary computation]],<ref>[https://ti.arc.nasa.gov/m/pub-archive/768h/0768%20(Hornby).pdf Gregory S. Hornby (2003). Generative Representations for Computer-Automated Design Systems, NASA Ames Research Center, Mail Stop 269–3, Moffett Field, CA 94035-1000]</ref><ref>[https://msu.edu/~jclune/webfiles/publications/2011-CluneLipson-Evolving3DObjectsWithCPPNs-ECAL.pdf J. Clune and H. Lipson (2011). Evolving three-dimensional objects with a generative encoding inspired by developmental biology. Proceedings of the European Conference on Artificial Life. 2011.]</ref> and {{gli|swarm intelligence}} algorithms.<ref>{{Cite journal |last1=Zhan |first1=Z.H. |display-authors=etal |year=2009 |title=Adaptive Particle Swarm Optimization |journal=IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics |volume=39 |issue=6 |pages=1362–1381 |doi=10.1109/tsmcb.2009.2015956 |pmid=19362911|s2cid=11191625 |url=https://eprints.gla.ac.uk/7645/1/7645.pdf }}</ref>}}

{{term|[[computer audition]] (CA)}}
{{defn|See {{gli|machine listening}}.}}

{{term|[[computer science]]}}
{{defn|The theory, experimentation, and engineering that form the basis for the design and use of [[computer]]s. It involves the study of {{gli|algorithm|algorithms}} that process, store, and communicate [[digital data|digital]] [[information]]. A [[computer scientist]] specializes in the theory of computation and the design of computational systems.<ref>{{Cite web |url=https://wordnetweb.princeton.edu/perl/webwn?s=computer%20scientist |title=WordNet Search—3.1 |publisher=Wordnetweb.princeton.edu |access-date=14 May 2012 |archive-date=14 January 2013 |archive-url=https://web.archive.org/web/20130114223028/http://wordnetweb.princeton.edu/perl/webwn?s=computer%20scientist |url-status=dead }}</ref>}}

{{term|[[computer vision]]}}
{{defn|An [[Interdisciplinarity|interdisciplinary scientific field]] that deals with how computers can be made to gain high-level understanding from [[digital image]]s or [[video]]s. From the perspective of [[engineering]], it seeks to automate tasks that the [[human visual system]] can do.<ref>Dana H. Ballard; Christopher M. Brown (1982). Computer Vision. Prentice Hall. {{ISBN|0-13-165316-4}}.</ref><ref>Huang, T. (1996-11-19). Vandoni, Carlo, E, ed. Computer Vision : Evolution And Promise (PDF). 19th CERN School of Computing. Geneva: CERN. pp. 21–25. {{doi|10.5170/CERN-1996-008.21}}. {{ISBN|978-9290830955}}.</ref><ref>Milan Sonka; Vaclav Hlavac; Roger Boyle (2008). Image Processing, Analysis, and Machine Vision. Thomson. {{ISBN|0-495-08252-X}}.</ref>}}

{{term|[[concept drift]]}}
{{defn|In {{gli|predictive analytics}} and {{gli|machine learning}}, the concept drift means that the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways. This causes problems because the predictions become less accurate as time passes.}}

{{term|[[connectionism]]}}
{{defn|An approach in the fields of [[cognitive science]], that hopes to explain [[mind|mental]] phenomena using {{gli|artificial neural network|artificial neural networks}}.<ref>{{Cite book |last1=Garson |first1=James |url=https://plato.stanford.edu/archives/fall2018/entries/connectionism/ |title=The Stanford Encyclopedia of Philosophy |date=27 November 2018 |publisher=Metaphysics Research Lab, Stanford University |editor-last=Zalta |editor-first=Edward N. |via=Stanford Encyclopedia of Philosophy}}</ref>}}

{{term|[[consistent heuristic]]}}
{{defn|In the study of [[shortest path problem|path-finding problem]]s in {{gli|artificial intelligence}}, a [[heuristic function]] is said to be consistent, or monotone, if its estimate is always less than or equal to the estimated distance from any neighboring vertex to the goal, plus the cost of reaching that neighbor.}}

{{term|[[constrained conditional model]] (CCM)}}
{{defn|A {{gli|machine learning}} and inference framework that augments the learning of conditional (probabilistic or discriminative) models with declarative constraints.}}

{{term|[[constraint logic programming]]}}
{{defn|A form of [[constraint programming]], in which [[logic programming]] is extended to include concepts from [[constraint satisfaction]]. A constraint logic program is a logic program that contains constraints in the body of clauses. An example of a clause including a constraint is {{code|2=prolog|A(X,Y) :- X+Y>0, B(X), C(Y)}}. In this clause, {{code|2=prolog|X+Y>0}} is a constraint; <code>A(X,Y)</code>, <code>B(X)</code>, and <code>C(Y)</code> are [[Literal (mathematical logic)|literal]]s as in regular logic programming. This clause states one condition under which the statement <code>A(X,Y)</code> holds: <code>X+Y</code> is greater than zero and both <code>B(X)</code> and <code>C(Y)</code> are true.}}

{{term|[[constraint programming]]}}
{{defn|A [[programming paradigm]] wherein [[Finitary relation|relation]]s between [[variable (mathematics)|variable]]s are stated in the form of [[constraint (mathematics)|constraint]]s. Constraints differ from the common [[Language primitive|primitive]]s of [[imperative programming]] languages in that they do not specify a step or sequence of steps to execute, but rather the properties of a solution to be found.}}

{{term|[[constructed language]]}}
{{ghat|Also '''conlang'''.}} 
{{defn|A language whose [[phonology]], [[grammar]], and [[vocabulary]] are consciously devised, instead of having developed [[natural language|naturally]]. Constructed languages may also be referred to as artificial, planned, or invented languages.<ref>{{Cite web |url=https://eurovision.tv/page/news?id=554&_t=ishtar_for_belgium_to_belgrade |title=Ishtar for Belgium to Belgrade |publisher=European Broadcasting Union |access-date=19 May 2013}}</ref>}}

{{term|[[control theory]]}}
{{defn|In [[control systems engineering]] is a subfield of mathematics that deals with the control of continuously operating [[dynamical system]]s in engineered processes and machines. The objective is to develop a control model for controlling such systems using a control action in an optimum manner without ''delay or overshoot'' and ensuring control [[Stability theory|stability]].}}

{{term|[[convolutional neural network]]}}
{{defn|In {{gli|deep learning}}, a convolutional neural network (CNN, or ConvNet) is a class of deep {{gli|neural network}} most commonly applied to image analysis. CNNs use a variation of [[multilayer perceptron]]s designed to require minimal [[Data pre-processing|preprocessing]].<ref name="LeCun">{{Cite web |url=https://yann.lecun.com/exdb/lenet/ |title=LeNet-5, convolutional neural networks |last1=LeCun |first1=Yann |access-date=16 November 2013}}</ref> They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on their shared-weights architecture and [[translation invariance]] characteristics.<ref>Zhang, Wei (1988). "Shift-invariant pattern recognition neural network and its optical architecture". Proceedings of annual conference of the Japan Society of Applied Physics.</ref><ref>{{Cite journal |last1=Zhang |first1=Wei |year=1990 |title=Parallel distributed processing model with local space-invariant interconnections and its optical architecture |journal=Applied Optics |volume=29 |issue=32 |pages=4790–7 |bibcode=1990ApOpt..29.4790Z |doi=10.1364/AO.29.004790 |pmid=20577468}}</ref>}}

{{term|[[crossover (genetic algorithm)|crossover]]}}
{{ghat|Also '''recombination'''.}}
{{defn|In {{gli|genetic algorithm|genetic algorithms}} and {{gli|evolutionary computation}}, a [[genetic operator]] used to combine the [[chromosome (genetic algorithm)|genetic information]] of two parents to generate new offspring. It is one way to stochastically generate new [[candidate solution|solution]]s from an existing population, and analogous to the [[chromosomal crossover|crossover]] that happens during [[sexual reproduction]] in biological organisms. Solutions can also be generated by [[cloning]] an existing solution, which is analogous to [[asexual reproduction]]. Newly generated solutions are typically {{gli|mutation|mutated}} before being added to the population.}}
{{glossaryend}}

{{Compact TOC|side=yes|center=yes|top=yes|num=yes|extlinks=yes|seealso=yes|refs=yes|nobreak=yes|}}

==D==
{{glossary}}
{{term|[[Darkforest]]}}
{{defn|A [[computer go]] program developed by [[Facebook]], based on {{gli|deep learning}} techniques using a [[convolutional neural network]]. Its updated version <!-- not a typo -->Darkfores2<!-- not a typo --> combines the techniques of its predecessor with [[Monte Carlo tree search]].<ref name="facebook-paper2">{{Cite arXiv |eprint=1511.06410v1 |class=cs.LG |first1=Yuandong |last1=Tian |first2=Yan |last2=Zhu |title=Better Computer Go Player with Neural Network and Long-term Prediction |year=2015}}</ref><ref>{{Cite web |url=https://technologyreview.com/s/544181/how-facebooks-ai-researchers-built-a-game-changing-go-engine/ |title=How Facebook's AI Researchers Built a Game-Changing Go Engine |date=December 4, 2015 |website=MIT Technology Review |access-date=2016-02-03}}</ref> The MCTS effectively takes tree search methods commonly seen in computer chess programs and randomizes them.<ref>{{Cite web |url=https://techtimes.com/articles/128636/20160128/facebook-ai-go-player-gets-smarter-with-neural-network-and-long-term-prediction-to-master-worlds-hardest-game.htm |title=Facebook AI Go Player Gets Smarter With Neural Network And Long-Term Prediction To Master World's Hardest Game |date=2016-01-28 |website=Tech Times |access-date=2016-04-24}}</ref> With the update, the system is known as Darkfmcts3.<ref name=":2">{{Cite web |url=https://venturebeat.com/2016/01/26/facebooks-artificially-intelligent-go-player-is-getting-smarter/ |title=Facebook's artificially intelligent Go player is getting smarter |date=2016-01-27 |website=VentureBeat |access-date=2016-04-24}}</ref>}}

{{term|[[Dartmouth workshop]]}}
{{defn|The Dartmouth Summer Research Project on Artificial Intelligence was the name of a 1956 summer workshop now considered by many<ref>Solomonoff, R.J.The Time Scale of Artificial Intelligence; Reflections on Social Effects, Human Systems Management, Vol 5 1985, Pp 149–153</ref><ref>Moor, J., The Dartmouth College Artificial Intelligence Conference: The Next Fifty years, AI Magazine, Vol 27, No., 4, Pp. 87–9, 2006</ref> (though not all<ref>Kline, Ronald R., Cybernetics, Automata Studies and the Dartmouth Conference on Artificial Intelligence, IEEE Annals of the History of Computing, October–December, 2011, IEEE Computer Society</ref>) to be the [[wikt:seminal|seminal]] event for {{gli|artificial intelligence}} as a field.}}

{{term|[[data augmentation]]}}
{{defn|Data augmentation in data analysis are techniques used to increase the amount of data. It helps reduce {{gli|overfitting}} when training a learning {{gli|algorithm}}.}}

{{term|[[data fusion]]}}
{{defn|The process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source.<ref name="dca">{{Cite journal |last1=Haghighat |first1=Mohammad |last2=Abdel-Mottaleb |first2=Mohamed |last3=Alhalabi |first3=Wadee |year=2016 |title=Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition |url=https://zenodo.org/record/889881 |journal=IEEE Transactions on Information Forensics and Security |volume=11 |issue=9 |pages=1984–1996 |doi=10.1109/TIFS.2016.2569061|s2cid=15624506 }}</ref>}}

{{term|[[data integration]]}}
{{defn|The process of combining [[data]] residing in different sources and providing users with a unified view of them.<ref name="refone">{{Cite conference |last1=Lenzerini |first1=Maurizio |year=2002 |title=Data Integration: A Theoretical Perspective |url=https://dis.uniroma1.it/~lenzerin/homepagine/talks/TutorialPODS02.pdf |pages=233–246 |book-title=PODS 2002 |access-date=5 July 2022 |archive-date=27 October 2021 |archive-url=https://web.archive.org/web/20211027091431/https://www.dis.uniroma1.it/~lenzerin/homepagine/talks/TutorialPODS02.pdf |url-status=dead }}</ref> This process becomes significant in a variety of situations, which include both commercial (such as when two similar companies need to merge their [[database]]s) and scientific (combining research results from different [[bioinformatics]] repositories, for example) domains. Data integration appears with increasing frequency as the volume (that is, [[big data]]) and the need to share existing data [[Information explosion|explode]]s.<ref name="DataExplode">{{Cite news |last1=Lane |first1=Frederick |url=https://toptechnews.com/story.xhtml?story_id=01300000E3D0&full_skip=1 |title=IDC: World Created 161 Billion Gigs of Data in 2006 |year=2006}}</ref> It has become the focus of extensive theoretical work, and numerous open problems remain unsolved.}}

{{term|[[data mining]]}}
{{defn|The process of discovering patterns in large data sets involving methods at the intersection of {{gli|machine learning}}, statistics, and database systems.}}

{{term|[[data science]]}}
{{defn|An interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract [[knowledge]] and insights from [[data]] in various forms, both structured and unstructured,<ref>{{Cite journal |last1=Dhar |first1=V. |year=2013 |title=Data science and prediction |url=https://cacm.acm.org/magazines/2013/12/169933-data-science-and-prediction/fulltext |journal=Communications of the ACM |volume=56 |issue=12 |pages=64–73 |doi=10.1145/2500499|s2cid=6107147 }}</ref><ref>{{Cite web |url=https://simplystatistics.org/2013/12/12/the-key-word-in-data-science-is-not-data-it-is-science/ |title=The key word in 'Data Science' is not Data, it is Science |last1=Leek |first1=Jeff |author-link=Jeffrey T. Leek |date=2013-12-12 |publisher=Simply Statistics |access-date=11 November 2018 |archive-date=2 January 2014 |archive-url=https://web.archive.org/web/20140102194117/https://simplystatistics.org/2013/12/12/the-key-word-in-data-science-is-not-data-it-is-science/ |url-status=dead }}</ref> similar to [[data mining]]. Data science is a "concept to unify statistics, data analysis, {{gli|machine learning}}, and their related methods" in order to "understand and analyze actual phenomena" with data.<ref name="Hayashi">{{Cite book |last1=Hayashi |first1=Chikio |chapter=What is Data Science ? Fundamental Concepts and a Heuristic Example |title=Data Science, Classification, and Related Methods |date=1998-01-01 |publisher=Springer Japan |isbn=9784431702085 |editor-last=Hayashi |editor-first=Chikio |series=Studies in Classification, Data Analysis, and Knowledge Organization |pages=40–51 |language=en |doi=10.1007/978-4-431-65950-1_3 |editor-last2=Yajima |editor-first2=Keiji |editor-last3=Bock |editor-first3=Hans-Hermann |editor-last4=Ohsumi |editor-first4=Noboru |editor-last5=Tanaka |editor-first5=Yutaka |editor-last6=Baba |editor-first6=Yasumasa |chapter-url=https://springer.com/book/9784431702085}}</ref> It employs techniques and theories drawn from many fields within the context of [[mathematics]], [[statistics]], [[information science]], and [[computer science]].}}

{{term|[[data set]]}}
{{ghat|Also '''dataset'''.}}
{{defn|A collection of [[data]]. Most commonly a data set corresponds to the contents of a single [[database table]], or a single statistical [[data matrix (multivariate statistics)|data matrix]], where every [[column (database)|column]] of the table represents a particular variable, and each [[row (database)|row]] corresponds to a given member of the data set in question. The data set lists values for each of the variables, such as height and weight of an object, for each member of the data set. Each value is known as a datum. The data set may comprise data for one or more members, corresponding to the number of rows.}}

{{term|[[data warehouse]] (DW or DWH)}}
{{ghat|Also '''enterprise data warehouse''' ('''EDW''').}}
{{defn|A system used for [[Financial reporting|reporting]] and [[data analysis]].<ref>{{Cite conference |last1=Dedić |first1=Nedim |last2=Stanier |first2=Clare |year=2016 |editor-last=Hammoudi |editor-first=Slimane |editor2-last=Maciaszek |editor2-first=Leszek |editor3-last=Missikoff |editor3-first=Michele M. Missikoff |editor4-last=Camp |editor4-first=Olivier |editor5-last=Cordeiro |editor5-first=José |title=An Evaluation of the Challenges of Multilingualism in Data Warehouse Development |url=https://eprints.staffs.ac.uk/2770/ |conference=International Conference on Enterprise Information Systems, 25–28 April 2016, Rome, Italy |publisher=SciTePress |volume=1 |pages=196–206 |doi=10.5220/0005858401960206 |isbn=978-989-758-187-8 |doi-access=free |conference-url=https://eprints.staffs.ac.uk/2770/1/ICEIS_2016_Volume_1.pdf |journal=Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016)}}</ref> DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place<ref name="rjmetrics">{{Cite web |url=https://blog.rjmetrics.com/2014/12/04/10-common-mistakes-when-building-a-data-warehouse/ |title=9 Reasons Data Warehouse Projects Fail |date=4 December 2014 |publisher=blog.rjmetrics.com |access-date=2017-04-30}}</ref>}}

{{term|[[Datalog]]}}
{{defn|A [[Declarative programming|declarative]] {{gli|logic programming}} language that syntactically is a subset of {{gli|Prolog}}. It is often used as a [[query language]] for [[deductive database]]s. In recent years, Datalog has found new application in [[data integration]], [[information extraction]], [[Computer network|networking]], [[program analysis]], [[security]], and [[cloud computing]].<ref>{{Citation |last1=Huang |last2=Green |last3=Loo |title=SIGMOD 2011 |url=https://cs.ucdavis.edu/~green/papers/sigmod906t-huang.pdf |contribution=Datalog and Emerging applications |publisher=UC Davis |access-date=5 July 2022 |archive-date=1 July 2022 |archive-url=https://web.archive.org/web/20220701172125/https://www.cs.ucdavis.edu/~green/papers/sigmod906t-huang.pdf |url-status=dead }}.</ref>}}

{{term|[[decision boundary]]}}
{{defn|In the case of {{gli|backpropagation}}-based {{gli|artificial neural network|artificial neural networks}} or {{gli|perceptron|perceptrons}}, the type of decision boundary that the network can learn is determined by the number of {{gli|hidden layer|hidden layers}} in the network. If it has no hidden layers, then it can only learn linear problems. If it has one hidden layer, then it can learn any [[continuous function]] on [[Compact space|compact subset]]s of [[Euclidean space|R<sup>n</sup>]] as shown by the [[Universal approximation theorem]], thus it can have an arbitrary decision boundary.}}

{{term|[[decision support system]] (DSS)}}
{{defn|Aan [[information system]] that supports business or organizational [[decision-making]] activities. DSSs serve the management, operations and planning levels of an organization (usually mid and higher management) and help people make decisions about problems that may be rapidly changing and not easily specified in advance—i.e. unstructured and semi-structured decision problems. Decision support systems can be either fully computerized or human-powered, or a combination of both.}}

{{term|[[decision theory]]}}
{{ghat|Also '''theory of choice'''.}}
{{defn|The study of the reasoning underlying an [[agent (economics)|agent's]] choices.<ref>Steele, Katie and Stefánsson, H. Orri, "Decision Theory", The Stanford Encyclopedia of Philosophy (Winter 2015 Edition), Edward N. Zalta (ed.), URL = [https://plato.stanford.edu/archives/win2015/entries/decision-theory]</ref> Decision theory can be broken into two branches: [[Norm (philosophy)|normative]] decision theory, which gives advice on how to make the [[optimal decision|best decision]]s given a set of uncertain beliefs and a set of [[Value (personal and cultural)|value]]s, and descriptive decision theory which analyzes how existing, possibly irrational agents actually make decisions.}}

{{term|[[decision tree learning]]}}
{{defn|Uses a [[decision tree]] (as a [[predictive model]]) to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in [[statistics]], [[data mining]] and {{gli|machine learning}}.}}

{{term|[[declarative programming]]}}
{{defn|A [[programming paradigm]]—a style of building the structure and elements of computer programs—that expresses the logic of a [[computation]] without describing its [[control flow]].<ref>{{Citation |last1=Lloyd |first1=J.W. |title=Practical Advantages of Declarative Programming}}</ref>}}

{{term|[[deductive classifier]]}}
{{defn|A type of {{gli|artificial intelligence}} [[inference engine]]. It takes as input a set of declarations in a [[frame language]] about a domain such as medical research or molecular biology. For example, the names of [[Class hierarchy|classes, sub-classes]], properties, and restrictions on allowable values.}}

{{term|[[Deep Blue (chess computer)|Deep Blue]]}}
{{defn|was a [[Computer chess|chess-playing computer]] developed by [[IBM]]. It is known for being the first computer chess-playing system to win both a chess game and a chess match against a reigning world champion under regular time controls.}}

{{term|[[deep learning]]}}
{{defn|A subset of {{gli|machine learning}} that focuses on utilizing {{gli|neural network|neural networks}} to perform tasks such as {{gli|classification}}, {{gli|regression}}, and {{gli|representation learning}}. The field takes inspiration from [[Neuroscience|biological neuroscience]] and is centered around stacking [[artificial neuron]]s into layers and "training" them to process data. The adjective "deep" refers to the use of multiple layers (ranging from three to several hundred or thousands) in the network. Methods used can be either {{gli|supervised learning|supervised}}, {{gli|semi-supervised learning|semi-supervised}}, or {{gli|unsupervised learning|unsupervised}}.}}

{{term|[[DeepMind Technologies]]}}
{{defn|A British {{gli|artificial intelligence}} company founded in September 2010, currently owned by [[Alphabet Inc.]] The company is based in [[London]], with research centres in [[Canada]],<ref>{{Cite web |url=https://deepmind.com/about/ |title=About Us {{!}} DeepMind |website=DeepMind|date=17 December 2024 }}</ref> [[France]],<ref>{{Cite web |url=https://deepmind.com/blog/a-return-to-paris/ |title=A return to Paris {{!}} DeepMind |website=DeepMind|date=29 March 2018 }}</ref> and the [[United States]]. [[List of mergers and acquisitions by Google|Acquired]] by [[Google]] in 2014, the company has created a {{gli|neural network}} that learns how to play [[video game]]s in a fashion similar to that of humans,<ref name="arxiv medium">{{Cite web |url=https://medium.com/the-physics-arxiv-blog/the-last-ai-breakthrough-deepmind-made-before-google-bought-it-for-400m-7952031ee5e1 |title=The Last AI Breakthrough DeepMind Made Before Google Bought It |date=2014-01-29 |publisher=The Physics [[arXiv]] Blog |access-date=12 October 2014}}</ref> as well as a [[neural Turing machine]],<ref name="arxiv">{{Cite arXiv |eprint=1410.5401 |class=cs.NE |first1=Alex |last1=Graves |first2=Greg |last2=Wayne |author-link=Alex Graves (computer scientist) |title=Neural Turing Machines |last3=Danihelka |first3=Ivo |year=2014}}</ref> or a neural network that may be able to access an external memory like a conventional [[Turing machine]], resulting in a computer that mimics the [[short-term memory]] of the human brain.<ref>[https://technologyreview.com/view/533741/best-of-2014-googles-secretive-deepmind-startup-unveils-a-neural-turing-machine/ Best of 2014: Google's Secretive DeepMind Startup Unveils a "Neural Turing Machine"] {{Webarchive|url=https://web.archive.org/web/20151204081728/http://www.technologyreview.com/view/533741/best-of-2014-googles-secretive-deepmind-startup-unveils-a-neural-turing-machine/ |date=4 December 2015 }}, ''[[MIT Technology Review]]''</ref><ref name="DNCnature2016">{{Cite journal |last1=Graves |first1=Alex |author-link=Alex Graves (computer scientist) |last2=Wayne |first2=Greg |last3=Reynolds |first3=Malcolm |last4=Harley |first4=Tim |last5=Danihelka |first5=Ivo |last6=Grabska-Barwińska |first6=Agnieszka |last7=Colmenarejo |first7=Sergio Gómez |last8=Grefenstette |first8=Edward |last9=Ramalho |first9=Tiago |date=12 October 2016 |title=Hybrid computing using a neural network with dynamic external memory |journal=Nature  |volume=538 |issue=7626 |pages=471–476 |bibcode=2016Natur.538..471G |doi=10.1038/nature20101 |issn=1476-4687 |pmid=27732574|s2cid=205251479 |url=https://ora.ox.ac.uk/objects/uuid:dd8473bd-2d70-424d-881b-86d9c9c66b51 }}</ref> The company made headlines in 2016 after its [[AlphaGo]] program beat human professional [[Go (game)|Go]] player [[Lee Sedol]], the world champion, in [[AlphaGo versus Lee Sedol|a five-game match]], which was the subject of a documentary film.<ref>{{Citation |last1=Kohs |first1=Greg |title=AlphaGo |date=29 September 2017 |url=https://imdb.com/title/tt6700846/ |others=Ioannis Antonoglou, Lucas Baker, Nick Bostrom |access-date=9 January 2018}}</ref> A more general program, [[AlphaZero]], beat the most powerful programs playing [[Go (game)|Go]], [[chess]], and [[shogi]] (Japanese chess) after a few days of play against itself using {{gli|reinforcement learning}}.<ref>{{Cite arXiv |eprint=1712.01815 |class=cs.AI |first1=David |last1=Silver |first2=Thomas |last2=Hubert |author-link=David Silver (programmer) |title=Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm |date=5 December 2017 |first3=Julian |last3=Schrittwieser |first4=Ioannis |last4=Antonoglou |first5=Matthew |last5=Lai |first6=Arthur |last6=Guez |first7=Marc |last7=Lanctot |first8=Laurent |last8=Sifre |first9=Dharshan |last9=Kumaran |first10=Thore |last10=Graepel |first11=Timothy |last11=Lillicrap |first12=Karen |last12=Simonyan |first13=Demis |last13=Hassabis |author-link13=Demis Hassabis}}</ref>}}

{{term|[[default logic]]}}
{{defn|A [[non-monotonic logic]] proposed by [[Raymond Reiter]] to formalize reasoning with default assumptions.}}

{{anchor|DBSCAN}}{{term|[[DBSCAN|Density-based spatial clustering of applications with noise]] (DBSCAN)}}
{{defn|A {{gli|cluster analysis|clustering}} {{gli|algorithm}} proposed by [[Martin Ester]], [[Hans-Peter Kriegel]], [[Jörg Sander]], and [[Xiaowei Xu]] in 1996.<ref name="dbscan">{{Cite conference
 | author1-link = Martin Ester | author1-first = Martin | author1-last = Ester | author2-link = Hans-Peter Kriegel | author2-first = Hans-Peter | author2-last =Kriegel | first3 = Jörg | last3 = Sander | first4 = Xiaowei | last4 = Xu
 | title = A density-based algorithm for discovering clusters in large spatial databases with noise
 | url = https://cdn.aaai.org/KDD/1996/KDD96-037.pdf
 |  pages = 226–231
 | editor1-first = Evangelos | editor1-last = Simoudis | editor2-first = Jiawei | editor2-last = Han | editor3-first = Usama M. | editor3-last = Fayyad
 | conference = Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96)
 | publisher = [[AAAI Press]]
 | year = 1996
 | isbn = 1-57735-004-9 
 | citeseerx = 10.1.1.121.9220
}}</ref>}}

{{term|[[description logic]] (DL)}}
{{defn|A family of formal [[knowledge representation]] languages. Many DLs are more expressive than [[propositional logic]] but less expressive than [[first-order logic]]. In contrast to the latter, the core reasoning problems for DLs are (usually) {{gli|decision problem|decidable}}, and efficient decision procedures have been designed and implemented for these problems. There are general, spatial, temporal, spatiotemporal, and fuzzy descriptions logics, and each description logic features a different balance between DL expressivity and {{gli|knowledge representation and reasoning|reasoning}} [[Complexity class|complexity]] by supporting different sets of mathematical constructors.<ref>{{Cite book |last1=Sikos |first1=Leslie F. |url=https://springer.com/us/book/9783319540658 |title=Description Logics in Multimedia Reasoning |date=2017 |publisher=Springer International Publishing |isbn=978-3-319-54066-5 |location=Cham |doi=10.1007/978-3-319-54066-5|s2cid=3180114 }}</ref>}}

{{term|[[developmental robotics]] (DevRob)}}
{{ghat|Also '''epigenetic robotics'''.}}
{{defn|A scientific field which aims at studying the developmental mechanisms, architectures, and constraints that allow lifelong and open-ended learning of new skills and new knowledge in embodied [[machine]]s.}}

{{term|[[diagnosis (artificial intelligence)|diagnosis]]}}
{{defn|Concerned with the development of algorithms and techniques that are able to determine whether the behaviour of a system is correct. If the system is not functioning correctly, the algorithm should be able to determine, as accurately as possible, which part of the system is failing, and which kind of fault it is facing. The computation is based on ''observations'', which provide information on the current behaviour.}}

{{term|[[dialogue system]]}}
{{ghat|Also '''conversational agent''' ('''CA''').}}
{{defn|A computer system intended to converse with a human with a coherent structure. Dialogue systems have employed text, speech, graphics, haptics, gestures, and other modes for communication on both the input and output channel.}}

{{term|[[diffusion model]]}}
{{defn|In {{gli|machine learning}}, '''diffusion models''', also known as '''diffusion probabilistic models''' or '''score-based generative models''', are a class of [[latent variable model]]s. They are [[Markov chain]]s trained using [[variational inference]].<ref name=":0ai">{{cite book |last1=Ho |first1=Jonathan |last2=Jain |first2=Ajay |last3=Abbeel |first3=Pieter |title=Denoising Diffusion Probabilistic Models |date=19 June 2020 |arxiv=2006.11239}}</ref> The goal of diffusion models is to learn the latent structure of a dataset by modeling the way in which data points diffuse through the [[latent space]]. In [[computer vision]], this means that a neural network is trained to [[denoise]] images blurred with [[Gaussian noise]] by learning to reverse the diffusion process.<ref name=":1ai">{{cite arXiv |last1=Song |first1=Yang |last2=Sohl-Dickstein |first2=Jascha |last3=Kingma |first3=Diederik P. |last4=Kumar |first4=Abhishek |last5=Ermon |first5=Stefano |last6=Poole |first6=Ben |date=2021-02-10 |title=Score-Based Generative Modeling through Stochastic Differential Equations |class=cs.LG |eprint=2011.13456}}</ref><ref>{{cite arXiv |last1=Gu |first1=Shuyang |last2=Chen |first2=Dong |last3=Bao |first3=Jianmin |last4=Wen |first4=Fang |last5=Zhang |first5=Bo |last6=Chen |first6=Dongdong |last7=Yuan |first7=Lu |last8=Guo |first8=Baining |title=Vector Quantized Diffusion Model for Text-to-Image Synthesis |date=2021 |class=cs.CV |eprint=2111.14822}}</ref> It mainly consists of three major components: the forward process, the reverse process, and the sampling procedure.<ref name="chang23design">{{cite arXiv |last1=Chang |first1=Ziyi |last2=Koulieris |first2=George Alex |last3=Shum |first3=Hubert P. H. |title=On the Design Fundamentals of Diffusion Models: A Survey |date=2023 |eprint=2306.04542 |class=cs.LG}}</ref> Three examples of generic diffusion modeling frameworks used in computer vision are denoising diffusion probabilistic models, noise conditioned score networks, and stochastic differential equations.<ref>{{cite journal |last1=Croitoru |first1=Florinel-Alin |last2=Hondru |first2=Vlad |last3=Ionescu |first3=Radu Tudor |last4= Shah |first4= Mubarak |title=Diffusion Models in Vision: A Survey |journal=IEEE Transactions on Pattern Analysis and Machine Intelligence |date=2023 |volume=45 |issue=9 |pages=10850–10869 |doi=10.1109/TPAMI.2023.3261988 |pmid=37030794 |arxiv=2209.04747|s2cid=252199918 }}</ref>}}

{{term|[[Dijkstra's algorithm]]}}
{{defn|An {{gli|algorithm}} for finding the [[Shortest path problem|shortest paths]] between [[Vertex (graph theory)|node]]s in a weighted [[Graph (abstract data type)|graph]], which may represent, for example, [[road network]]s.}}

{{term|[[dimensionality reduction]]}}
{{ghat|Also '''dimension reduction'''.}} 
{{defn|The process of reducing the number of random variables under consideration<ref>{{Cite journal |last1=Roweis |first1=S. T. |last2=Saul |first2=L. K. |year=2000 |title=Nonlinear Dimensionality Reduction by Locally Linear Embedding |journal=Science |volume=290 |issue=5500 |pages=2323–2326 |bibcode=2000Sci...290.2323R |citeseerx=10.1.1.111.3313 |doi=10.1126/science.290.5500.2323 |pmid=11125150|s2cid=5987139}}</ref> by obtaining a set of principal variables. It can be divided into [[feature selection]] and [[feature extraction]].<ref>{{Cite book |last1=Pudil |first1=P. |title=Feature Extraction, Construction and Selection |url=https://archive.org/details/featureextractio00liuh |url-access=limited |last2=Novovičová |first2=J. |year=1998 |isbn=978-1-4613-7622-4 |editor-last=Liu |editor-first=Huan |pages=[https://archive.org/details/featureextractio00liuh/page/n120 101] |chapter=Novel Methods for Feature Subset Selection with Respect to Problem Knowledge |doi=10.1007/978-1-4615-5725-8_7 |editor-last2=Motoda |editor-first2=Hiroshi}}</ref>}}

{{term|[[discrete system]]}}
{{defn|Any system with a countable number of states. Discrete systems may be contrasted with continuous systems, which may also be called analog systems. A final discrete system is often modeled with a directed [[Graph (discrete mathematics)|graph]] and is analyzed for correctness and complexity according to [[computational theory]]. Because discrete systems have a countable number of states, they may be described in precise [[mathematical model]]s. A [[computer]] is a [[finite-state machine]] that may be viewed as a discrete system. Because computers are often used to model not only other discrete systems but continuous systems as well, methods have been developed to represent real-world continuous systems as discrete systems. One such method involves sampling a continuous signal at [[discrete time]] intervals.}}

{{term|[[distributed artificial intelligence]] (DAI)}}
{{ghat|Also '''decentralized artificial intelligence'''.}}
{{defn|A subfield of {{gli|artificial intelligence}} research dedicated to the development of distributed solutions for problems. DAI is closely related to and a predecessor of the field of {{gli|multi-agent system|multi-agent systems}}.<ref>Demazeau, Yves, and J-P. Müller, eds. Decentralized Ai. Vol. 2. Elsevier, 1990.</ref>}}

{{term|[[double descent]]}}
{{defn|A phenomenon in [[statistics]] and {{gli|machine learning}} where a [[Statistical model|model]] with a small number of [[parameter]]s and a model with an extremely large number of parameters have a small [[Training, validation, and test data sets|test error]], but a model whose number of parameters is about the same as the number of [[data points]] used to train the model will have a large error.<ref>{{Cite web |date=2019-12-05 |title=Deep Double Descent |url=https://openai.com/blog/deep-double-descent/ |access-date=2022-08-12 |website=OpenAI |language=en}}</ref> This phenomenon has been considered surprising, as it contradicts assumptions about {{gli|overfitting}} in classical machine learning.<ref>{{Cite arXiv |eprint=2303.14151v1 |class=cs.LG |first1=Rylan |last1=Schaeffer |first2=Mikail |last2=Khona |title=Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle |date=2023-03-24 |language=en |last3=Robertson |first3=Zachary |last4=Boopathy |first4=Akhilan |last5=Pistunova |first5=Kateryna |last6=Rocks |first6=Jason W. |last7=Fiete |first7=Ila Rani |last8=Koyejo |first8=Oluwasanmi}}</ref>}}

{{anchor|dropout}}{{term|[[Dilution (neural networks)|dropout]]}}
{{ghat|Also '''dilution'''.}}
{{defn|A {{gli|regularization}} technique for reducing {{gli|overfitting}} in {{gli|artificial neural network|artificial neural networks}} by preventing complex co-adaptations on training data.}}

{{term|[[dynamic epistemic logic]] (DEL)}}
{{defn|A logical framework dealing with knowledge and information change. Typically, DEL focuses on situations involving multiple [[Intelligent agent|agent]]s and studies how their knowledge changes when [[Event (philosophy)|event]]s occur.}}

{{glossaryend}}

{{Compact TOC|side=yes|center=yes|top=yes|num=yes|extlinks=yes|seealso=yes|refs=yes|nobreak=yes|}}

==E==
{{glossary}}
{{term|[[eager learning]]}}
{{defn|A learning method in which the system tries to construct a general, input-independent target function during training of the system, as opposed to [[lazy learning]], where {{gli|generalization}} beyond the training data is delayed until a query is made to the system.<ref>{{Cite conference |last1=Hendrickx |first1=Iris |last2=Van den Bosch, Antal |author-link2=Antal van den Bosch |date=October 2005 |title=Hybrid algorithms with Instance-Based Classification |url=https://books.google.com/books?id=GtcevX7n90wC&pg=PA158 |publisher=Springer |pages=158–169 |isbn=9783540292432 |book-title=Machine Learning: ECML2005}}</ref>}}

{{term|[[early stopping]]}}
{{defn|A {{gli|regularization}} technique often used when training a {{gli|machine learning}} model with an iterative method such as [[gradient descent]].}}

{{term|[[Ebert test]]}}
{{defn|A test which gauges whether a computer-based [[speech synthesis|synthesized voice]]<ref name=twsL35/><ref name="twsL34">{{Cite news |last1=Lee |first1=Jennifer |url=https://bits.blogs.nytimes.com/2011/03/07/roger-ebert-tests-his-vocal-cords-and-comedic-delivery/?src=me |title=Roger Ebert Tests His Vocal Cords, and Comedic Delivery |date=March 7, 2011 |work=The New York Times |access-date=2011-09-12 |quote=Now perhaps, there is the Ebert Test, a way to see if a synthesized voice can deliver humor with the timing to make an audience laugh.... He proposed the Ebert Test as a way to gauge the humanness of a synthesized voice.}}</ref> can tell a [[humor|joke]] with sufficient skill to cause people to [[laugh]].<ref name="twsL41">{{Cite news |url=https://tips-tricks.co.in/2011/03/roger-eberts-inspiring-digital.html |title=Roger Ebert's Inspiring Digital Transformation |date=March 5, 2011 |access-date=2011-09-12 |url-status=dead |archive-url=https://web.archive.org/web/20110325160035/https://tips-tricks.co.in/2011/03/roger-eberts-inspiring-digital.html |archive-date=25 March 2011 |publisher=Tech News |quote=Meanwhile, the technology that enables Ebert to "speak" continues to see improvements – for example, adding more realistic inflection for question marks and exclamation points. In a test of that, which Ebert called the "Ebert test" for computerized voices,}}</ref> It was proposed by [[film critic]] [[Roger Ebert]] at the [[TED (conference)|2011 TED conference]] as a challenge to [[software developer]]s to have a computerized voice master the inflections, delivery, timing, and intonations of a speaking human.<ref name="twsL35">{{Cite news |last1=Ostrow |first1=Adam |url=https://mashable.com/2011/03/05/roger-ebert-ted-talk/ |title=Roger Ebert's Inspiring Digital Transformation |date=March 5, 2011 |access-date=2011-09-12 |publisher=Mashable Entertainment |quote=With the help of his wife, two colleagues and the Alex-equipped MacBook that he uses to generate his computerized voice, famed film critic Roger Ebert delivered the final talk at the TED conference on Friday in Long Beach, California....}}</ref> The test is similar to the [[Turing test]] proposed by [[Alan Turing]] in 1950 as a way to gauge a computer's ability to exhibit intelligent behavior by generating performance indistinguishable from a [[human being]].<ref name="twsL37">{{Cite news |last1=Pasternack |first1=Alex |url=https://motherboard.tv/2011/4/18/a-macbook-may-have-given-roger-ebert-his-voice-but-an-ipod-saved-his-life-video |title=A MacBook May Have Given Roger Ebert His Voice, But An iPod Saved His Life (Video) |date=Apr 18, 2011 |access-date=2011-09-12 |url-status=dead |archive-url=https://web.archive.org/web/20110906063605/https://motherboard.tv/2011/4/18/a-macbook-may-have-given-roger-ebert-his-voice-but-an-ipod-saved-his-life-video |archive-date=6 September 2011 |publisher=Motherboard |quote=He calls it the "Ebert Test," after Turing's AI standard...}}</ref>}}

{{term|[[echo state network]] (ESN)}}
{{defn|A [[recurrent neural network]] with a sparsely connected {{gli|hidden layer}} (with typically 1% connectivity). The connectivity and weights of hidden neurons are fixed and randomly assigned. The weights of output neurons can be learned so that the network can (re)produce specific temporal patterns. The main interest of this network is that although its behaviour is non-linear, the only weights that are modified during training are for the synapses that connect the hidden neurons to output neurons. Thus, the error function is quadratic with respect to the parameter vector and can be differentiated easily to a linear system.<ref>{{Cite journal |last1=Jaeger |first1=Herbert |last2=Haas |first2=Harald |year=2004 |title=Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication |url=https://columbia.edu/cu/biology/courses/w4070/Reading_List_Yuste/haas_04.pdf |journal=Science |volume=304 |issue=5667 |pages=78–80 |doi=10.1126/science.1091277 |pmid=15064413 |bibcode=2004Sci...304...78J |s2cid=2184251 |access-date=5 July 2022 |archive-date=1 September 2022 |archive-url=https://web.archive.org/web/20220901054532/http://www.columbia.edu/cu/biology/courses/w4070/Reading_List_Yuste/haas_04.pdf |url-status=dead }}</ref><ref>Herbert Jaeger (2007) [https://scholarpedia.org/article/Echo_State_Network Echo State Network.] {{Webarchive|url=https://web.archive.org/web/20220628153329/http://www.scholarpedia.org/article/Echo_state_network |date=28 June 2022 }} Scholarpedia.</ref>}}

{{term|[[embodied agent]]}}
{{ghat|Also '''interface agent'''.}}
{{defn|An {{gli|intelligent agent}} that interacts with the environment through a physical body within that environment. Agents that are represented graphically with a body, for example a human or a cartoon animal, are also called embodied agents, although they have only virtual, not physical, embodiment.<ref>{{Cite journal |last1=Serenko |first1=Alexander |last2=Bontis |first2=Nick |last3=Detlor |first3=Brian |year=2007 |title=End-user adoption of animated interface agents in everyday work applications |url=https://aserenko.com/papers/Serenko_Bontis_Detlor_end_user_adoption_agent.pdf |journal=Behaviour and Information Technology |volume=26 |issue=2 |pages=119–132 |doi=10.1080/01449290500260538|s2cid=2175427 }}</ref>}}

{{term|[[embodied cognitive science]]}}
{{defn|An interdisciplinary field of research, the aim of which is to explain the mechanisms underlying intelligent behavior. It comprises three main methodologies: 1) the modeling of psychological and biological systems in a holistic manner that considers the mind and body as a single entity, 2) the formation of a common set of general principles of intelligent behavior, and 3) the experimental use of robotic agents in controlled environments.}}

{{term|[[error-driven learning]]}}
{{defn|A sub-area of {{gli|machine learning}} concerned with how an {{gli|intelligent agent|agent}} ought to take actions in an [[Environment (systems)|environment]] so as to minimize some error feedback. It is a type of {{gli|reinforcement learning}}.}}

{{term|[[ensemble learning]]}}
{{defn|The use of multiple {{gli|machine learning}} algorithms to obtain better [[predictive inference|predictive performance]] than could be obtained from any of the constituent learning algorithms alone.<ref>{{cite journal
 |last1=Opitz |first1=D. |last2=Maclin |first2=R. |title=Popular ensemble methods: An empirical study
 |journal=[[Journal of Artificial Intelligence Research]]
 |volume=11 |pages=169–198 |year=1999
 |doi=10.1613/jair.614
|doi-access=free|arxiv=1106.0257}}</ref><ref>{{cite journal
 |last1=Polikar |first1=R. |title=Ensemble based systems in decision making
 |journal=IEEE Circuits and Systems Magazine
 |volume=6 |issue=3 |pages=21–45 |year=2006
 |doi=10.1109/MCAS.2006.1688199
|s2cid=18032543
 }}</ref><ref name="Rokach2010">{{cite journal
 |last1=Rokach |first1=L. |title=Ensemble-based classifiers
 |journal=Artificial Intelligence Review
 |volume=33
 |issue=1–2 |pages=1–39 |year=2010
 |doi=10.1007/s10462-009-9124-7
|s2cid=11149239
 |hdl=11323/1748
 |hdl-access=free
 }}</ref>}}

{{anchor|epoch}}{{term|epoch}}
{{defn|In {{gli|machine learning}}, particularly in the creation of {{gli|artificial neural network|artificial neural networks}}, an epoch is training the model for one cycle through the full training dataset. Small models are typically trained for as many epochs as it takes to reach the best performance on the validation dataset. The largest models may train for only one epoch.}}

{{term|[[ethics of artificial intelligence]]}}
{{defn|The part of the [[ethics of technology]] specific to artificial intelligence.}}

{{anchor|evolutionary algorithm}}{{term|[[evolutionary algorithm]] (EA)}}
{{defn|A subset of {{gli|evolutionary computation}},<ref name="EVOALG">{{Cite book |last1=Vikhar |first1=P. A. |title=2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC) |chapter=Evolutionary algorithms: A critical review and its future prospects |year=2016 |publisher=Jalgaon, 2016, pp. 261–265 |pages=261–265 |doi=10.1109/ICGTSPICC.2016.7955308 |isbn=978-1-5090-0467-6|s2cid=22100336 }}</ref> a generic population-based [[metaheuristic]] [[optimization]] {{gli|algorithm}}. An EA uses mechanisms inspired by [[biological evolution]], such as [[reproduction]], [[mutation]], [[genetic recombination|recombination]], and [[natural selection|selection]]. [[Candidate solution]]s to the [[optimization problem]] play the role of individuals in a population, and the [[fitness function]] determines the quality of the solutions (see also [[loss function]]). [[Evolution]] of the population then takes place after the repeated application of the above operators.}}

{{term|[[evolutionary computation]]}}
{{defn|A family of {{gli|algorithm|algorithms}} for [[global optimization]] inspired by [[biological evolution]], and the subfield of {{gli|artificial intelligence}} and [[soft computing]] studying these algorithms. In technical terms, they are a family of population-based [[trial and error]] problem solvers with a [[metaheuristic]] or [[stochastic optimization]] character.}}

{{term|[[evolving classification function]] (ECF)}}
{{defn|Evolving classification functions are used for {{gli|classification|classifying}} and {{gli|cluster analysis|clustering}} in the field of {{gli|machine learning}} and {{gli|artificial intelligence}}, typically employed for [[data stream mining]] tasks in dynamic and changing environments.}}

{{term|[[existential risk from artificial general intelligence|existential risk]]}}
{{defn|The hypothesis that substantial progress in {{gli|artificial general intelligence}} (AGI) could someday result in [[human extinction]] or some other unrecoverable [[global catastrophe]].<ref name="aima">{{Cite book |last1=Russell |first1=Stuart |title=Artificial Intelligence: A Modern Approach |title-link=Artificial Intelligence: A Modern Approach |last2=Norvig |first2=Peter |date=2009 |publisher=Prentice Hall |isbn=978-0-13-604259-4 |chapter=26.3: The Ethics and Risks of Developing Artificial Intelligence |author-link=Stuart J. Russell |author-link2=Peter Norvig}}</ref><ref>{{Cite journal |last1=Bostrom |first1=Nick |author-link=Nick Bostrom |year=2002 |title=Existential risks |journal=[[Journal of Evolution and Technology]] |volume=9 |issue=1 |pages=1–31}}</ref><ref>{{Cite news |url=https://slate.com/articles/technology/future_tense/2016/04/killer_a_i_101_a_cheat_sheet_to_the_terminology_the_ethical_debates_the.html |title=Your Artificial Intelligence Cheat Sheet |date=1 April 2016 |work=[[Slate (magazine)|Slate]] |access-date=16 May 2016}}</ref>}}

{{term|[[expert system]]}}
{{defn|A computer system that emulates the decision-making ability of a human expert.<ref name="Jackson1998">{{Citation |last1=Jackson |first1=Peter |title=Introduction To Expert Systems |page=2 |year=1998 |edition=3 |publisher=Addison Wesley |isbn=978-0-201-87686-4}}</ref> Expert systems are designed to solve complex problems by [[automated reasoning|reasoning]] through bodies of knowledge, represented mainly as [[Rule-based system|if–then rule]]s rather than through conventional [[procedural code]].<ref>{{Cite web |url=https://pcmag.com/encyclopedia_term/0,2542,t=conventional+programming&i=40325,00.asp |title=Conventional programming |work=PC Magazine |access-date=2013-09-15 |archive-date=14 October 2012 |archive-url=https://web.archive.org/web/20121014124656/https://pcmag.com/encyclopedia_term/0%2C2542%2Ct%3Dconventional+programming%26i%3D40325%2C00.asp |url-status=dead }}</ref>}}
{{glossaryend}}

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==F==
{{glossary}}
{{term|[[fast-and-frugal trees]]}}
{{defn|A type of [[classification tree]]. Fast-and-frugal trees can be used as decision-making tools which operate as lexicographic classifiers, and, if required, associate an action (decision) to each class or category.<ref name="naive">Martignon, Laura; Vitouch, Oliver; Takezawa, Masanori; Forster, Malcolm. [https://researchgate.net/publication/27278577_Naive_and_Yet_Enlightened_From_Natural_Frequencies_to_Fast_and_Frugal_Decision_Trees "Naive and Yet Enlightened: From Natural Frequencies to Fast and Frugal Decision Trees"], published in ''Thinking : Psychological perspectives on reasoning, judgement and decision making'' (David Hardman and Laura Macchi; editors), Chichester: John Wiley & Sons, 2003.</ref>}}

{{anchor|feature}}{{term|[[Feature (machine learning)|feature]]}}
{{defn|An individual measurable property or characteristic of a phenomenon.<ref name="ml">{{cite book |author=Bishop, Christopher |title=Pattern recognition and machine learning |publisher=Springer |location=Berlin |year=2006 |isbn=0-387-31073-8 }}</ref> In {{gli|computer vision}} and [[image processing]], a feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties. Features may be specific structures in an image (such as points, edges, or objects), or the result of a general [[neighborhood operation]] or feature detection applied to the image.}}

{{term|[[feature extraction]]}}
{{defn|In {{gli|machine learning}}, [[pattern recognition]], and [[image processing]], feature extraction starts from an initial set of measured data and builds derived values ({{gli|feature|features}}) intended to be informative and non-redundant, facilitating the subsequent learning and {{gli|generalization}} steps, and in some cases leading to better human interpretations.}}

{{anchor|representation learning}}
{{term|[[feature learning]]}}
{{ghat|Also '''representation learning'''.}}
{{defn|In {{gli|machine learning}}, {{gli|feature}} learning or representation learning<ref name="pami">{{Cite journal |last1=Bengio |first1=Y. |last2=Courville |first2=A. |last3=Vincent |first3=P. |year=2013 |title=Representation Learning: A Review and New Perspectives |journal=IEEE Transactions on Pattern Analysis and Machine Intelligence |volume=35 |issue=8 |pages=1798–1828 |arxiv=1206.5538 |doi=10.1109/tpami.2013.50 |pmid=23787338|s2cid=393948 }}</ref> is a set of techniques that allows a system to automatically discover the representations needed for feature detection or {{gli|classification}} from raw data. This replaces manual [[feature engineering]] and allows a machine to both learn the features and use them to perform a specific task.}}

{{term|[[feature selection]]}}
{{defn|In {{gli|machine learning}} and [[statistics]], feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant {{gli|feature|features}} (variables, predictors) for use in model construction.}}

{{term|[[federated learning]]}}
{{defn|A {{gli|machine learning}} technique that allows for training models on multiple devices with decentralized data, thus helping preserve the privacy of individual users and their data.}}

{{term|[[first-order logic]]}}
{{ghat|Also '''first-order predicate calculus''' or '''predicate logic'''.}}
{{defn|A collection of [[formal system]]s used in [[mathematics]], [[philosophy]], [[linguistics]], and [[computer science]]. First-order logic uses [[Quantification (logic)|quantified variable]]s over non-logical objects and allows the use of sentences that contain variables, so that rather than propositions such as ''Socrates is a man'' one can have expressions in the form "there exists X such that X is [[Socrates]] and X is a man" and ''there exists'' is a quantifier while ''X'' is a variable.<ref name="auto">Hodgson, Dr. J. P. E., [https://people.sju.edu/~jhodgson/ugai/1order.html "First Order Logic"] {{Webarchive|url=https://web.archive.org/web/20190921071136/http://people.sju.edu/~jhodgson/ugai/1order.html |date=21 September 2019 }}, [[Saint Joseph's University]], [[Philadelphia]], 1995.</ref> This distinguishes it from [[propositional logic]], which does not use quantifiers or relations.<ref>[[George Edward Hughes|Hughes, G. E.]], & [[Max Cresswell|Cresswell, M. J.]], ''A New Introduction to Modal Logic'' ([[London]]: [[Routledge]], 1996), [https://books.google.com/books?id=_CB5wiBeaA4C&pg=PA161 p.161].</ref>}}

{{term|[[fluent (artificial intelligence)|fluent]]}}
{{defn|A condition that can change over time. In logical approaches to reasoning about actions, fluents can be represented in [[first-order logic]] by [[Predicate (logic)|predicate]]s having an argument that depends on time.}}

{{term|[[formal language]]}}
{{defn|A set of [[String (computer science)|word]]s whose [[Symbol (formal)|letter]]s are taken from an [[Alphabet (computer science)|alphabet]] and are [[Well-formedness|well-formed]] according to a specific set of rules.}}

{{term|[[forward chaining]]}}
{{ghat|Also '''forward reasoning'''.}} 
{{defn|One of the two main methods of reasoning when using an [[inference engine]] and can be described [[logically]] as repeated application of ''[[modus ponens]]''. Forward chaining is a popular implementation strategy for [[expert system]]s, [[business rules engine|business]]es and [[Production system (computer science)|production rule system]]s. The opposite of forward chaining is {{gli|backward chaining}}. Forward chaining starts with the available [[data]] and uses inference rules to extract more data (from an end user, for example) until a [[goal]] is reached. An [[inference engine]] using forward chaining searches the inference rules until it finds one where the [[antecedent (logic)|antecedent]] (If clause) is known to be true. When such a rule is found, the engine can conclude, or infer, the [[consequent]] (Then clause), resulting in the addition of new [[information]] to its data.<ref>{{Cite book |last1=Feigenbaum |first1=Edward |url=https://archive.org/details/riseofexpertco00feig |title=The Rise of the Expert Company |publisher=Times Books |year=1988 |isbn=978-0-8129-1731-4 |page=[https://archive.org/details/riseofexpertco00feig/page/318 318] |url-access=registration}}</ref>}}

{{term|[[frame (artificial intelligence)|frame]]}}
{{defn|An artificial intelligence [[data structure]] used to divide [[knowledge]] into substructures by representing "[[stereotype]]d situations". Frames are the primary data structure used in artificial intelligence {{gli|frame language}}.}}

{{term|[[frame language]]}}
{{defn|A technology used for [[knowledge representation]] in artificial intelligence. Frames are stored as [[ontologies]] of [[Set theory|set]]s and subsets of the [[Frame (artificial intelligence)|frame concept]]s. They are similar to class hierarchies in [[object-oriented language]]s although their fundamental design goals are different. Frames are focused on explicit and intuitive representation of knowledge whereas objects focus on [[Encapsulation (object-oriented programming)|encapsulation]] and [[information hiding]]. Frames originated in AI research and objects primarily in [[software engineering]]. However, in practice the techniques and capabilities of frame and object-oriented languages overlap significantly.}}

{{term|[[frame problem]]}}
{{defn|The problem of finding adequate collections of axioms for a viable description of a robot environment.<ref>{{Cite journal |last1=Hayes |first1=Patrick |title=The Frame Problem and Related Problems in Artificial Intelligence |url=https://aitopics.org/sites/default/files/classic/Webber-Nilsson-Readings/Rdgs-NW-Hayes-FrameProblem.pdf |publisher=University of Edinburgh |journal=Readings in Artificial Intelligence |access-date=9 March 2019 |archive-date=3 December 2013 |archive-url=https://web.archive.org/web/20131203002046/https://aitopics.org/sites/default/files/classic/Webber-Nilsson-Readings/Rdgs-NW-Hayes-FrameProblem.pdf |url-status=dead |date=1981 |pages=223–230 |doi=10.1016/B978-0-934613-03-3.50020-9|isbn=9780934613033 |s2cid=141711662 }}</ref>}}

{{term|[[friendly artificial intelligence]]}}
{{ghat|Also '''friendly AI''' or '''FAI'''.}}
{{defn|A hypothetical {{gli|artificial general intelligence}} (AGI) that would have a positive effect on humanity. It is a part of the [[ethics of artificial intelligence]] and is closely related to [[machine ethics]]. While machine ethics is concerned with how an artificially intelligent agent should behave, friendly artificial intelligence research is focused on how to practically bring about this behaviour and ensuring it is adequately constrained.}}

{{term|[[futures studies]]}}
{{defn|The study of postulating possible, probable, and preferable [[future]]s and the worldviews and myths that underlie them.<ref name="Sardar, Z. 2010 pp. 177">{{Cite journal |last1=Sardar |first1=Z |year=2010 |title=The Namesake: Futures; futures studies; futurology; futuristic; Foresight – What's in a name? |journal=Futures |volume=42 |issue=3 |pages=177–184 |doi=10.1016/j.futures.2009.11.001}}</ref>}}

{{term|[[fuzzy control system]]}}
{{defn|A [[control system]] based on {{gli|fuzzy logic}}—a [[mathematical]] system that analyzes [[analog signal|analog]] input values in terms of [[mathematical logic|logic]]al variables that take on continuous values between 0 and 1, in contrast to classical or [[Digital data|digital]] logic, which operates on discrete values of either 1 or 0 (true or false, respectively).<ref name="Pedrycz">{{Cite book |last1=Pedrycz |first1=Witold |title=Fuzzy control and fuzzy systems |publisher=Research Studies Press Ltd. |year=1993 |edition=2}}</ref><ref name="Hájek">{{Cite book |last1=Hájek |first1=Petr |title=Metamathematics of fuzzy logic |publisher=Springer Science & Business Media |year=1998 |edition=4}}</ref>}}

{{term|[[fuzzy logic]]}}
{{defn|A simple form for the [[many-valued logic]], in which the [[truth value]]s of variables may have any degree of "''Truthfulness''" that can be represented by any real number in the range between 0 (as in Completely False) and 1 (as in Completely True) inclusive. Consequently, It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false. In contrast to [[Boolean logic]], where the truth values of variables may have the integer values 0 or 1 only.}}

{{term|[[fuzzy rule]]}}
{{defn|A rule used within {{gli|fuzzy logic|fuzzy logic systems}} to infer an output based on input variables.}}

{{term|[[fuzzy set]]}}
{{defn|In classical [[set theory]], the membership of elements in a set is assessed in binary terms according to a [[Principle of bivalence|bivalent condition]] — an element either belongs or does not belong to the set. By contrast, fuzzy set theory permits the gradual assessment of the membership of elements in a set; this is described with the aid of a [[Membership function (mathematics)|membership function]] valued in the real unit interval [0,&nbsp;1]. Fuzzy sets generalize classical sets, since the [[indicator function]]s (aka ''characteristic functions'') of classical sets are special cases of the membership functions of fuzzy sets, if the latter only take values 0 or 1.<ref name=":0">D. Dubois and H. Prade (1988) Fuzzy Sets and Systems. Academic Press, New York.</ref> In fuzzy set theory, classical bivalent sets are usually called [[Crisp set|''crisp'' set]]s. The fuzzy set theory can be used in a wide range of domains in which information is incomplete or imprecise, such as [[bioinformatics]].<ref>{{Cite journal |last1=Liang |first1=Lily R. |last2=Lu |first2=Shiyong |last3=Wang |first3=Xuena |last4=Lu |first4=Yi |last5=Mandal |first5=Vinay |last6=Patacsil |first6=Dorrelyn |last7=Kumar |first7=Deepak |year=2006 |title=FM-test: A fuzzy-set-theory-based approach to differential gene expression data analysis |journal=BMC Bioinformatics |volume=7 |issue=Suppl 4 |pages=S7 |doi=10.1186/1471-2105-7-S4-S7 |pmc=1780132 |pmid=17217525 |doi-access=free }}</ref>}}
{{glossaryend}}

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==G==
{{glossary}}
{{term|[[game theory]]}}
{{defn|The study of [[mathematical model]]s of strategic interaction between rational decision-makers.<ref name="Myerson">[[Roger B. Myerson|Myerson, Roger B.]] (1991). ''Game Theory: Analysis of Conflict,'' Harvard University Press, p.&nbsp;[https://books.google.com/books?id=E8WQFRCsNr0C&pg=PA1 1]. Chapter-preview links, pp. [https://books.google.com/books?id=E8WQFRCsNr0C&pg=PR7 vii–xi].</ref>}}

{{term|[[general game playing]] (GGP)}}
{{defn|General game playing is the design of artificial intelligence programs to be able to run and play more than one game successfully.<ref>{{cite web |last1=Pell |first1=Barney |editor1=H. van den Herik |editor2=L. Allis |title=Metagame: a new challenge for games and learning. |date=1992 |url=https://svn.sable.mcgill.ca/sable/courses/COMP763/oldpapers/pell-92-metagame.pdf |trans-title=Heuristic programming in artificial intelligence 3–the third computerolympiad |location=Ellis-Horwood |access-date=13 June 2020 |archive-date=17 February 2020 |archive-url=https://web.archive.org/web/20200217154408/https://svn.sable.mcgill.ca/sable/courses/COMP763/oldpapers/pell-92-metagame.pdf |url-status=dead }}</ref><ref name="pellfirst">{{cite journal |last1=Pell |first1=Barney |title=A Strategic Metagame Player for General Chess-Like Games |journal=Computational Intelligence |date=1996 |volume=12 |issue=1 |pages=177–198 |doi=10.1111/j.1467-8640.1996.tb00258.x |s2cid=996006 |language=en |issn=1467-8640}}</ref><ref name="ggppell">{{cite journal |last1=Genesereth |first1=Michael |last2=Love |first2=Nathaniel |last3=Pell |first3=Barney |title=General Game Playing: Overview of the AAAI Competition |journal=AI Magazine |date=15 June 2005 |volume=26 |issue=2 |pages=62 |doi=10.1609/aimag.v26i2.1813 |language=en |issn=2371-9621}}</ref>}}

{{anchor|generalization}}{{term|[[Generalization (learning)|generalization]]}}
{{defn|The concept that humans, other animals, and {{gli|artificial neural network|artificial neural networks}} use past learning in present situations of learning if the conditions in the situations are regarded as similar.<ref>{{cite book|last1=Gluck|first1=Mark A.|last2=Mercado|first2=Eduardo|last3=Myers|first3=Catherine E.|title=Learning and memory: from brain to behavior|date=2011|publisher=Worth Publishers|location=New York|isbn=9781429240147|page=209|edition=2nd}}</ref>}}

{{term|[[generalization error]]}}
{{defn|For {{gli|supervised learning}} applications in {{gli|machine learning}} and [[statistical learning theory]], '''generalization error'''<ref name="Mohri, M. 2018">Mohri, M., Rostamizadeh A., Talwakar A., (2018) ''Foundations of Machine learning'', 2nd ed., Boston: MIT Press</ref> (also known as the '''out-of-sample error'''<ref>Y S. Abu-Mostafa, M.Magdon-Ismail, and H.-T. Lin (2012) Learning from Data, AMLBook Press. {{ISBN|978-1600490064}}</ref> or the '''risk''') is a measure of how accurately a learning [[algorithm]] is able to predict outcomes for previously unseen data.}}

{{term|[[generative adversarial network]] (GAN)}}
{{defn|A class of {{gli|machine learning}} systems. Two {{gli|neural network|neural networks}} contest with each other in a [[zero-sum game]] framework.}}

{{term|[[generative artificial intelligence]]}}
{{defn|Generative artificial intelligence is [[artificial intelligence]] capable of generating text, images, or other media in response to [[Prompt engineering|prompts]].<ref name="nytimes">{{Cite web|url=https://www.nytimes.com/2023/01/27/technology/anthropic-ai-funding.html|title=Anthropic Said to Be Closing In on $300 Million in New A.I. Funding|last1=Griffith|first1=Erin|last2=Metz|first2=Cade|date=2023-01-27|work=[[The New York Times]]|access-date=2023-03-14}}</ref><ref name="bloomberg">{{cite news |last1=Lanxon |first1=Nate |last2=Bass |first2=Dina |last3=Davalos |first3=Jackie |title=A Cheat Sheet to AI Buzzwords and Their Meanings |url=https://news.bloomberglaw.com/tech-and-telecom-law/a-cheat-sheet-to-ai-buzzwords-and-their-meanings-quicktake |access-date=March 14, 2023 |newspaper=Bloomberg News |date=March 10, 2023 |location=}}</ref> Generative AI models {{gli|machine learning|learn}} the patterns and structure of their input [[training data]] and then generate new data that has similar characteristics, typically using {{gli|transformer}}-based {{gli|deep learning|deep}} {{gli|neural network|neural networks}}.<ref>{{Cite news |last=Pasick |first=Adam |date=2023-03-27 |title=Artificial Intelligence Glossary: Neural Networks and Other Terms Explained |language=en-US |work=The New York Times |url=https://www.nytimes.com/article/ai-artificial-intelligence-glossary.html |access-date=2023-04-22 |issn=0362-4331}}</ref><ref>{{cite web | url=https://openai.com/research/generative-models | title=Generative models | author1=Andrej Karpathy | author2=Pieter Abbeel | author3=Greg Brockman | author4=Peter Chen | author5=Vicki Cheung | author6=Yan Duan | author7=Ian Goodfellow | author8=Durk Kingma | author9=Jonathan Ho | author10=Rein Houthooft | author11=Tim Salimans | author12=John Schulman | author13=Ilya Sutskever | author14=Wojciech Zaremba | date=2016-06-16 | website=OpenAI}}</ref>}}

{{term|[[generative pretrained transformer]] (GPT)}}
{{defn|A {{gli|large language model}} based on the {{gli|transformer}} architecture that generates text. It is first pretrained to predict the next [[Lexical analysis|token]] in texts (a token is typically a word, subword, or punctuation). After their pretraining, GPT models can generate human-like text by repeatedly predicting the token that they would expect to follow. GPT models are usually also fine-tuned, for example with {{gli|reinforcement learning from human feedback}} to reduce {{gli|hallucination}} or harmful behaviour, or to format the output in a conversationnal format.<ref>{{Cite web |last=Smith |first=Craig S. |date=March 15, 2023 |title=ChatGPT-4 Creator Ilya Sutskever on AI Hallucinations and AI Democracy |url=https://www.forbes.com/sites/craigsmith/2023/03/15/gpt-4-creator-ilya-sutskever-on-ai-hallucinations-and-ai-democracy/ |access-date=2023-12-25 |website=Forbes |language=en}}</ref>}}

{{anchor|genetic algorithm}}{{term|[[genetic algorithm]] (GA)}}
{{defn|A [[metaheuristic]] inspired by the process of [[natural selection]] that belongs to the larger class of [[evolutionary algorithm]]s (EA). Genetic algorithms are commonly used to generate high-quality solutions to [[optimization]] and [[Search algorithm|search problem]]s by relying on bio-inspired operators such as [[Mutation (genetic algorithm)|mutation]], [[crossover (genetic algorithm)|crossover]] and [[selection (genetic algorithm)|selection]].{{sfn|Mitchell|1996|p=2}}}}

{{term|[[genetic operator]]}}
{{defn|An [[Operator (programming)|operator]] used in [[genetic algorithm]]s to guide the algorithm towards a solution to a given problem. There are three main types of operators ([[Mutation (genetic algorithm)|mutation]], [[Crossover (genetic algorithm)|crossover]] and [[selection (genetic algorithm)|selection]]), which must work in conjunction with one another in order for the algorithm to be successful.}}

{{term|[[glowworm swarm optimization]]}}
{{defn|A {{gli|swarm intelligence}} [[optimization]] {{gli|algorithm}} based on the behaviour of [[glowworm]]s (also known as fireflies or lightning bugs).}}

{{term|[[gradient boosting]]}}
{{defn|A {{gli|machine learning}} technique based on {{gli|boosting}} in a functional space, where the target is ''pseudo-residuals'' instead of [[Residuals (statistics)|residuals]] as in traditional boosting.}}

{{term|[[graph (abstract data type)]]}}
{{defn|In [[computer science]], a graph is an [[abstract data type]] that is meant to implement the [[Graph (discrete mathematics)|undirected graph]] and [[directed graph]] concepts from [[mathematics]]; specifically, the field of {{gli|graph theory}}.}}

{{term|[[graph (discrete mathematics)]]}}
{{defn|In mathematics, and more specifically in {{gli|graph theory}}, a graph is a structure amounting to a set of objects in which some pairs of the objects are in some sense "related". The objects correspond to mathematical abstractions called ''[[vertice]]s'' (also called ''nodes'' or ''points'') and each of the related pairs of vertices is called an ''edge'' (also called an ''arc'' or ''line'').<ref>{{Cite book |last1=Trudeau |first1=Richard J. |url=https://store.doverpublications.com/0486678709.html |title=Introduction to Graph Theory |publisher=Dover Pub. |year=1993 |isbn=978-0-486-67870-2 |edition=Corrected, enlarged republication. |location=New York |pages=19 |quote=A graph is an object consisting of two sets called its ''vertex set'' and its ''edge set''. |access-date=8 August 2012}}</ref>}}

{{term|[[graph database]] (GDB)}}
{{defn|A [[database]] that uses [[Graph (data structure)|graph structure]]s for [[Semantic query|semantic queries]] with [[Node (graph theory)|node]]s, [[Edge (graph theory)|edge]]s, and properties to represent and store data. A key concept of the system is the ''graph'' (or ''edge'' or ''relationship''), which directly relates data items in the store a collection of nodes of data and edges representing the relationships between the nodes. The relationships allow data in the store to be linked together directly, and in many cases retrieved with one operation. Graph databases hold the relationships between data as a priority. Querying relationships within a graph database is fast because they are perpetually stored within the database itself. Relationships can be intuitively visualized using graph databases, making it useful for heavily inter-connected data.<ref>{{Cite journal |last1=Yoon |first1=Byoung-Ha |last2=Kim |first2=Seon-Kyu |last3=Kim |first3=Seon-Young |date=March 2017 |title=Use of Graph Database for the Integration of Heterogeneous Biological Data |journal=Genomics & Informatics |volume=15 |issue=1 |pages=19–27 |doi=10.5808/GI.2017.15.1.19 |issn=1598-866X |pmc=5389944 |pmid=28416946}}</ref><ref>{{Cite book |last1=Bourbakis |first1=Nikolaos G. |url=https://books.google.com/books?id=mV3wxKLHlnwC&q=%22gdb%22+%22graph+database%22&pg=PA381 |title=Artificial Intelligence and Automation |publisher=World Scientific |year=1998 |isbn=9789810226374 |page=381 |access-date=2018-04-20}}</ref>}}

{{term|[[graph theory]]}}
{{defn|The study of ''[[graph (discrete mathematics)|graph]]s'', which are mathematical structures used to model pairwise relations between objects.}}

{{term|[[graph traversal]]}}
{{ghat|Also '''graph search'''.}}
{{defn|The process of visiting (checking and/or updating) each vertex in a [[Graph (discrete mathematics)|graph]]. Such traversals are classified by the order in which the vertices are visited. [[Tree traversal]] is a special case of graph traversal.}}
{{glossaryend}}

{{Compact TOC|side=yes|center=yes|top=yes|num=yes|extlinks=yes|seealso=yes|refs=yes|nobreak=yes|}}

==H==
{{glossary}}
{{term|[[Hallucination (artificial intelligence)|hallucination]]}}
{{defn|A response generated by AI that contains false or [[misleading information]] presented as [[fact]].}}

{{term|[[heuristic (computer science)|heuristic]]}}
{{defn|A technique designed for [[problem solving|solving a problem]] more quickly when classic methods are too slow, or for finding an approximate solution when classic methods fail to find any exact solution. This is achieved by trading optimality, completeness, [[accuracy]], or [[Accuracy and precision|precision]] for speed. In a way, it can be considered a shortcut. A heuristic function, also called simply a heuristic, is a [[Function (mathematics)|function]] that ranks alternatives in {{gli|search algorithm|search algorithms}} at each branching step based on available information to decide which branch to follow. For example, it may approximate the exact solution.<ref>{{Cite book |last1=Pearl |first1=Judea |title=Heuristics: intelligent search strategies for computer problem solving |url=https://archive.org/details/intelligentsearc00jude |url-access=limited |publisher=Addison-Wesley Pub. Co., Inc., Reading, MA |year=1984 |location=United States |page=[https://archive.org/details/intelligentsearc00jude/page/n21 3] |bibcode=1985hiss.book.....P |osti=5127296}}</ref>}}

{{term|[[hidden layer]]}}
{{defn|A layer of neurons in an {{gli|artificial neural network}} that is neither an input layer nor an output layer.}}

{{term|[[hyper-heuristic]]}}
{{defn|A {{gli|heuristic}} search method that seeks to automate the process of selecting, combining, generating, or adapting several simpler heuristics (or components of such heuristics) to efficiently solve computational search problems, often by the incorporation of {{gli|machine learning}} techniques. One of the motivations for studying hyper-heuristics is to build systems which can handle classes of problems rather than solving just one problem.<ref>E. K. Burke, E. Hart, [[Graham Kendall|G. Kendall]], J. Newall, P. Ross, and S. Schulenburg, Hyper-heuristics: An emerging direction in modern search technology, Handbook of Metaheuristics (F. Glover and G. Kochenberger, eds.), Kluwer, 2003, pp. 457–474.</ref><ref name="Ross05">P. Ross, Hyper-heuristics, Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques (E. K. Burke and [[Graham Kendall|G. Kendall]], eds.), Springer, 2005, pp. 529–556.</ref><ref name="Ozcan08">{{Cite journal |last1=Ozcan |first1=E. |last2=Bilgin |first2=B. |last3=Korkmaz |first3=E. E. |year=2008 |title=A Comprehensive Analysis of Hyper-heuristics |journal=Intelligent Data Analysis |volume=12 |issue=1 |pages=3–23 |doi=10.3233/ida-2008-12102}}</ref>}}

{{anchor|hyperparameter}}{{term|[[Hyperparameter (machine learning)|hyperparameter]]}}
{{defn|A [[Parameter (computer programming)|parameter]] that can be set in order to define any configurable part of a {{gli|machine learning}} model's learning process.}}

{{term|[[hyperparameter optimization]]}}
{{defn|The process of choosing a set of optimal {{gli|hyperparameter|hyperparameters}} for a learning {{gli|algorithm}}.}}

{{term|[[hyperplane]]}}
{{defn|A decision boundary in {{gli|machine learning}} {{gli|classification|classifiers}} that partitions the input space into two or more sections, with each section corresponding to a unique class label.}}
{{glossaryend}}

==I==
{{glossary}}
{{term|[[IEEE Computational Intelligence Society]]}}
{{defn|A [[professional society]] of the [[Institute of Electrical and Electronics Engineers]] (IEEE) focussing on "the theory, design, application, and development of biologically and linguistically motivated computational paradigms emphasizing {{gli|neural network|neural networks}}, connectionist systems, [[genetic algorithm]]s, [[evolutionary programming]], fuzzy systems, and hybrid intelligent systems in which these paradigms are contained".<ref name="IEEE CIS Scope">{{Cite web |url=https://cis.ieee.org/scope.html |title=IEEE CIS Scope |url-status=dead |archive-url=https://web.archive.org/web/20160604143046/https://cis.ieee.org/scope.html |archive-date=4 June 2016 |access-date=18 March 2019}}</ref>}}

{{term|[[incremental learning]]}}
{{defn|A method of {{gli|machine learning}}, in which input data is continuously used to extend the existing model's knowledge i.e. to further train the model. It represents a dynamic technique of {{gli|supervised learning|supervised}} and {{gli|unsupervised learning}} that can be applied when training data becomes available gradually over time or its size is out of system memory limits. Algorithms that can facilitate incremental learning are known as incremental machine learning algorithms.}}

{{term|[[inference engine]]}}
{{defn|A component of the system that applies logical rules to the knowledge base to deduce new information.}}

{{term|[[information integration]] (II)}}
{{defn|The merging of information from heterogeneous sources with differing conceptual, contextual and typographical representations. It is used in [[data mining]] and consolidation of data from unstructured or semi-structured resources. Typically, ''information integration'' refers to textual representations of knowledge but is sometimes applied to [[rich-media]] content. Information fusion, which is a related term, involves the combination of information into a new set of information towards reducing redundancy and uncertainty.<ref name="dca" />}}

{{term|[[Information Processing Language]] (IPL)}}
{{defn|A [[programming language]] that includes features intended to help with programs that perform simple problem solving actions such as lists, [[dynamic memory allocation]], [[data type]]s, [[Recursion (computer science)|recursion]], [[Subroutine|function]]s as arguments, generators, and [[cooperative multitasking]]. IPL invented the concept of list processing, albeit in an [[Assembly language|assembly-language]] style.}}

{{term|[[intelligence amplification]] (IA)}}
{{ghat|Also '''cognitive augmentation''', '''machine augmented intelligence''', and '''enhanced intelligence'''.}}
{{defn|The effective use of [[information technology]] in augmenting [[Intelligence#Human intelligence|human intelligence]].}}

{{term|[[intelligence explosion]]}}
{{defn|A possible outcome of humanity building {{gli|artificial general intelligence}} (AGI). AGI would be capable of recursive self-improvement leading to rapid emergence of ASI ([[Superintelligence|artificial superintelligence]]), the limits of which are unknown, at the time of the technological singularity.}}

{{anchor|intelligent agent}}{{term|[[intelligent agent]] (IA)}}
{{defn|An [[autonomous]] entity which acts, directing its activity towards achieving goals (i.e. it is an [[Software agent|agent]]), upon an [[Environment (biophysical)|environment]] using observation through sensors and consequent actuators (i.e. it is intelligent). Intelligent agents may also {{gli|machine learning|learn}} or use [[knowledge representation|knowledge]] to achieve their goals. They may be very simple or [[Complexity|very complex]].}}

{{term|[[intelligent control]]}}
{{defn|A class of [[Control theory|control]] techniques that use various {{gli|artificial intelligence}} computing approaches like [[artificial neural networks|neural network]]s, [[Bayesian probability]], {{gli|fuzzy logic}}, {{gli|machine learning}}, {{gli|reinforcement learning}}, [[evolutionary computation]] and [[genetic algorithm]]s.<ref>{{Cite web |url=https://engineering.purdue.edu/ManLab/control/intell_control.htm |title=Control of Machining Processes – Purdue ME Manufacturing Laboratories |website=engineering.purdue.edu}}</ref>}}

{{term|[[intelligent personal assistant]]}}
{{ghat|Also '''virtual assistant''' or '''personal digital assistant'''.}}
{{defn|A [[software agent]] that can perform tasks or services for an individual based on verbal commands. Sometimes the term "[[chatbot]]" is used to refer to virtual assistants generally or specifically accessed by [[online chat]] (or in some cases online chat programs that are exclusively for entertainment purposes). Some virtual assistants are able to interpret human speech and respond via synthesized voices. Users can ask their assistants questions, control [[home automation]] devices and media playback via voice, and manage other basic tasks such as email, to-do lists, and calendars with verbal commands.<ref>{{Cite journal |last1=Hoy |first1=Matthew B. |year=2018 |title=Alexa, Siri, Cortana, and More: An Introduction to Voice Assistants |journal=Medical Reference Services Quarterly |volume=37 |issue=1 |pages=81–88 |doi=10.1080/02763869.2018.1404391 |pmid=29327988|s2cid=30809087 }}</ref>}}

{{term|[[interpretation (logic)|interpretation]]}}
{{defn|An assignment of meaning to the [[symbol (formal)|symbol]]s of a {{gli|formal language}}. Many formal languages used in [[mathematics]], [[logic]], and [[theoretical computer science]] are defined in solely [[syntactic]] terms, and as such do not have any meaning until they are given some interpretation. The general study of interpretations of formal languages is called [[formal semantics (logic)|formal semantic]]s.}}

{{term|[[intrinsic motivation (artificial intelligence)|intrinsic motivation]]}}
{{defn|An [[intelligent agent]] is intrinsically motivated to act if the information content alone, of the experience resulting from the action, is the motivating factor. Information content in this context is measured in the [[information theory]] sense as quantifying uncertainty. A typical intrinsic motivation is to search for unusual (surprising) situations, in contrast to a typical extrinsic motivation such as the search for food. Intrinsically motivated artificial agents display behaviours akin to [[exploration]] and [[curiosity]].<ref>{{Cite book |last1=Oudeyer |first1=Pierre-Yves |title=Proc. of the 8th Conf. on Epigenetic Robotics |last2=Kaplan |first2=Frederic |date=2008 |volume=5 |pages=29–31 |chapter=How can we define intrinsic motivation?}}</ref>}}

{{term|[[issue tree]]}}
{{ghat|Also '''logic tree'''.}}
{{defn|A graphical breakdown of a question that dissects it into its different components vertically and that progresses into details as it reads to the right.<ref>{{Cite journal |last1=Chevallier |first1=Arnaud |s2cid=157255130 |title=Strategic thinking in complex problem solving |date=2016 |journal=Oxford Scholarship Online |publisher=[[Oxford University Press]] |isbn=9780190463908 |location=Oxford; New York |doi=10.1093/acprof:oso/9780190463908.001.0001 |oclc=940455195}}</ref>{{rp|47}}  Issue trees are useful in [[problem solving]] to identify the root causes of a problem as well as to identify its potential solutions. They also provide a reference point to see how each piece fits into the whole picture of a problem.<ref>{{Cite web |url=https://interactive.cabinetoffice.gov.uk/strategy/survivalguide/skills/s_issue.htm |title=Strategy survival guide: Issue trees |date=July 2004 |publisher=Government of the United Kingdom |location=London |archive-url=https://web.archive.org/web/20120217163843/https://interactive.cabinetoffice.gov.uk/strategy/survivalguide/skills/s_issue.htm |archive-date=2012-02-17 |access-date=2018-10-06 }} Also available in [http://webarchive.nationalarchives.gov.uk/20060213205515/https://strategy.gov.uk/downloads/survivalguide/downloads/ssg_v2.1.pdf PDF format].</ref>}}
{{glossaryend}}

{{Compact TOC|side=yes|center=yes|top=yes|num=yes|extlinks=yes|seealso=yes|refs=yes|nobreak=yes|}}

==J==
{{glossary}}
{{term|[[junction tree algorithm]]}}
{{ghat|Also '''Clique Tree'''.}}
{{defn|A method used in {{gli|machine learning}} to extract [[marginal distribution|marginalization]] in general [[Graph (discrete mathematics)|graph]]s. In essence, it entails performing [[belief propagation]] on a modified graph called a [[junction tree]]. The graph is called a tree because it branches into different sections of data; [[Vertex (graph theory)|node]]s of variables are the branches.<ref name=":1">{{cite web |url=https://ai.stanford.edu/~paskin/gm-short-course/lec3.pdf |title=A Short Course on Graphical Models |last1=Paskin |first1=Mark |website=Stanford}}</ref>}}
{{glossaryend}}

==K==
{{glossary}}
{{term|[[kernel method]]}}
{{defn|In {{gli|machine learning}}, kernel methods are a class of algorithms for [[pattern analysis]], whose best known member is the {{gli|support vector machine}} (SVM). The general task of pattern analysis is to find and study general types of relations (e.g., {{gli|cluster analysis}}, [[ranking]]s, [[principal component]]s, [[correlation]]s, {{gli|classification|classifications}}) in datasets.}}

{{term|[[KL-ONE]]}}
{{defn|A well-known [[knowledge representation]] system in the tradition of [[semantic network]]s and [[Frame (Artificial intelligence)|frame]]s; that is, it is a [[frame language]]. The system is an attempt to overcome semantic indistinctness in semantic network representations and to explicitly represent conceptual information as a structured inheritance network.<ref>{{Cite journal |last1=Woods |first1=W. A. |last2=Schmolze |first2=J. G. |year=1992 |title=The KL-ONE family |journal=Computers & Mathematics with Applications |volume=23 |issue=2–5 |pages=133 |doi=10.1016/0898-1221(92)90139-9 |author-link1=William Aaron Woods}}</ref><ref>{{Cite journal |last1=Brachman |first1=R. J. |last2=Schmolze |first2=J. G. |year=1985 |title=An Overview of the KL-ONE Knowledge Representation System |url=https://dli.iiit.ac.in/vdata/IJCAI/IJCAI-83-VOL-1/PDF/072.pdf |journal=Cognitive Science |volume=9 |issue=2 |pages=171 |doi=10.1207/s15516709cog0902_1 |author-link1=Ronald J. Brachman }}{{Dead link|date=September 2023 |bot=InternetArchiveBot |fix-attempted=yes }}</ref><ref>{{Cite book |last1=Duce |first1=D.A. |last2=Ringland |first2=G.A. |url=https://archive.org/details/approachestoknow0000unse |title=Approaches to Knowledge Representation, An Introduction |publisher=Research Studies Press, Ltd. |year=1988 |isbn=978-0-86380-064-1 |url-access=registration}}</ref>}}

{{anchor|k-nearest neighbors}}{{term|[[k-nearest neighbors algorithm|''k''-nearest neighbors]]}}
{{defn|A [[Non-parametric statistics|non-parametric]] {{gli|supervised learning}} method first developed by [[Evelyn Fix]] and [[Joseph Lawson Hodges Jr.|Joseph Hodges]] in 1951,<ref>{{Cite report | last1=Fix | first1=Evelyn | last2= Hodges |  first2=Joseph L. | title=Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties | issue=Report Number 4, Project Number 21-49-004 | year=1951 | url=https://apps.dtic.mil/dtic/tr/fulltext/u2/a800276.pdf | archive-url=https://web.archive.org/web/20200926212807/https://apps.dtic.mil/dtic/tr/fulltext/u2/a800276.pdf | url-status=live | archive-date=September 26, 2020 | publisher=USAF School of Aviation Medicine, Randolph Field, Texas}}</ref> and later expanded by [[Thomas M. Cover|Thomas Cover]].<ref name=":1" /> It is used for {{gli|classification}} and {{gli|regression}}.}}

{{term|[[knowledge acquisition]]}}
{{defn|The process used to define the rules and ontologies required for a [[knowledge-based system]]. The phrase was first used in conjunction with [[expert system]]s to describe the initial tasks associated with developing an expert system, namely finding and interviewing [[knowledge domain|domain]] experts and capturing their knowledge via [[Rule-based system|rule]]s, [[Object-oriented programming|object]]s, and [[Frame language|frame-based]] [[ontologies]].}}

{{anchor|knowledge-based system}}{{term|[[knowledge-based system]] (KBS)}}
{{defn|A [[computer program]] that [[automated reasoning|reason]]s and uses a [[knowledge base]] to [[problem solving|solve]] [[complex systems|complex problem]]s. The term is broad and refers to many different kinds of systems. The one common theme that unites all knowledge based systems is an attempt to represent knowledge explicitly and a [[reasoning system]] that allows it to derive new knowledge. Thus, a knowledge-based system has two distinguishing features: a [[knowledge base]] and an [[inference engine]].}}

{{term|[[knowledge distillation]]}}
{{defn|The process of transferring knowledge from a large {{gli|machine learning}} model to a smaller one.}}

{{term|[[knowledge engineering]] (KE)}}
{{defn|All technical, scientific, and social aspects involved in building, maintaining, and using {{gli|knowledge-based system|knowledge-based systems}}.}}

{{term|[[knowledge extraction]]}}
{{defn|The creation of [[Knowledge representation and reasoning|knowledge]] from structured ([[relational database]]s, [[XML]]) and unstructured ([[text (literary theory)|text]], documents, [[image]]s) sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must [[Knowledge representation and reasoning|represent knowledge]] in a manner that facilitates inferencing. Although it is methodically similar to [[information extraction]] and [[extract, transform, load|ETL]], the main criterion is that the extraction result goes beyond the creation of structured information or the transformation into a [[Database schema|relational schema]]. It requires either the reuse of existing [[Knowledge representation and reasoning|formal knowledge]] (reusing identifiers or [[ontologies]]) or the generation of a schema based on the source data.}}

{{term|[[knowledge Interchange Format]] (KIF)}}
{{defn|A computer language designed to enable systems to share and reuse information from [[knowledge-based system]]s. KIF is similar to [[frame language]]s such as [[KL-ONE]] and [[LOOM (ontology)|LOOM]] but unlike such language its primary role is not intended as a framework for the expression or use of knowledge but rather for the interchange of knowledge between systems. The designers of KIF likened it to [[PostScript]]. PostScript was not designed primarily as a language to store and manipulate documents but rather as an interchange format for systems and devices to share documents. In the same way KIF is meant to facilitate sharing of knowledge across different systems that use different languages, formalisms, platforms, etc.}}

{{anchor|knowledge representation and reasoning}}{{term|[[knowledge representation and reasoning]] (KR²<!-- won't work: <sup>2</sup>--> or KR&R)}}
{{defn|The field of {{gli|artificial intelligence}} dedicated to representing information about the world in a form that a computer system can utilize to solve complex tasks such as [[Computer-aided diagnosis|diagnosing a medical condition]] or [[natural language user interface|having a dialog in a natural language]]. Knowledge representation incorporates findings from psychology<ref>{{Cite book |last1=Schank |first1=Roger |title=Scripts, Plans, Goals, and Understanding: An Inquiry Into Human Knowledge Structures |last2=Robert Abelson |date=1977 |publisher=Lawrence Erlbaum Associates, Inc.}}</ref> about how humans solve problems and represent knowledge in order to design [[Formalism (mathematics)|formalism]]s that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from [[logic]] to automate various kinds of ''reasoning'', such as the application of rules or the relations of [[Set theory|set]]s and [[subset]]s.<ref>{{Cite news |url=https://deepminds.science/knowledge-representation-neural-networks/ |title=Knowledge Representation in Neural Networks – deepMinds |date=2018-08-16 |work=deepMinds |access-date=2018-08-16 |archive-date=17 August 2018 |archive-url=https://web.archive.org/web/20180817023355/https://deepminds.science/knowledge-representation-neural-networks/ |url-status=dead }}</ref> Examples of knowledge representation formalisms include [[semantic net]]s, [[systems architecture]], [[Frame (artificial intelligence)|frame]]s, rules, and [[ontologies]]. Examples of [[automated reasoning]] engines include [[inference engine]]s, [[automated theorem proving|theorem prover]]s, and classifiers.}}

{{term|[[k-means clustering]]}}
{{defn|A method of [[vector quantization]], originally from [[signal processing]], that aims to [[partition of a set|partition]] ''n'' observations into ''k'' {{gli|cluster analysis|clusters}} in which each observation belongs to the cluster with the nearest [[mean]] (cluster centers or cluster [[centroid]]), serving as a prototype of the cluster.}}
{{glossaryend}}

==L==
{{glossary}}

{{term|[[language model]]}}
{{defn|A probabilistic model that manipulates [[natural language]].}}

{{anchor|large language model}}{{term|[[large language model]] (LLM)}}
{{defn|A {{gli|language model}} with a large number of [[parameters]] (typically at least a billion) that are adjusted during training. Due to its size, it requires a lot of data and computing capability to train. Large language models are usually based on the {{gli|transformer}} architecture.<ref>{{Cite web |last=Kerner |first=Sean Michael |title=What are Large Language Models? |url=https://www.techtarget.com/whatis/definition/large-language-model-LLM |access-date=2024-01-28 |website=TechTarget |language=en}}</ref>}}

{{term|[[lazy learning]]}}
{{defn|In {{gli|machine learning}}, lazy learning is a learning method in which {{gli|generalization}} of the [[training data]] is, in theory, delayed until a query is made to the system, as opposed to in [[eager learning]], where the system tries to generalize the training data before receiving queries.}}

{{term|[[Lisp (programming language)]] (LISP)}}
{{defn|A family of [[programming language]]s with a long history and a distinctive, fully [[parenthesized]] [[Polish notation#Implementations|prefix notation]].<ref>{{Cite book |last1=Reilly |first1=Edwin D. |url=https://archive.org/details/milestonesincomp0000reil |title=Milestones in computer science and information technology |publisher=Greenwood Publishing Group |year=2003 |isbn=978-1-57356-521-9 |pages=[https://archive.org/details/milestonesincomp0000reil/page/156 156]–157 |url-access=registration}}</ref>}}

{{term|[[logic programming]]}}
{{defn|A type of [[programming paradigm]] which is largely based on [[formal logic]]. Any program written in a logic {{gli|programming language}} is a set of sentences in logical form, expressing facts and rules about some problem domain. Major logic programming language families include {{gli|Prolog}}, {{gli|answer set programming}} (ASP), and {{gli|Datalog}}.}}

{{anchor|long short-term memory}}{{term|[[long short-term memory]] (LSTM)}}
{{defn|An artificial {{gli|recurrent neural network}} architecture<ref>{{Cite journal |last1=Hochreiter |first1=Sepp |last2=Schmidhuber |first2=Jürgen |year=1997 |title=Long short-term memory |journal=Neural Computation |volume=9 |issue=8 |pages=1735–1780 |doi=10.1162/neco.1997.9.8.1735 |pmid=9377276|s2cid=1915014 }}</ref> used in the field of {{gli|deep learning}}. Unlike standard [[feedforward neural network]]s, LSTM has feedback connections that make it a "general purpose computer" (that is, it can compute anything that a {{gli|Turing machine}} can).<ref name="Siegelmann92">{{Cite book |last1=Siegelmann |first1=Hava T. |last2=Sontag |first2=Eduardo D. |title=Proceedings of the fifth annual workshop on Computational learning theory |chapter=On the computational power of neural nets |date=1992 |work=ACM |isbn=978-0897914970 |volume=COLT '92 |pages=440–449 |doi=10.1145/130385.130432|s2cid=207165680 }}</ref> It can not only process single data points (such as images), but also entire sequences of data (such as speech or video).}}
{{glossaryend}}

==M==
{{glossary}}
{{anchor|machine vision}}{{term|[[machine vision]] (MV)}}
{{defn|The technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, [[process control]], and robot guidance, usually in industry. Machine vision is a term encompassing a large number of technologies, software and hardware products, integrated systems, actions, methods and expertise. Machine vision as a [[systems engineering]] discipline can be considered distinct from [[computer vision]], a form of [[computer science]]. It attempts to integrate existing technologies in new ways and apply them to solve real world problems. The term is the prevalent one for these functions in industrial automation environments but is also used for these functions in other environments such as security and vehicle guidance.}}

{{term|[[Markov chain]]}}
{{defn|A [[stochastic model]] describing a [[sequence]] of possible events in which the probability of each event depends only on the state attained in the previous event.<ref>{{Cite web |url=https://en.oxforddictionaries.com/definition/us/markov_chain |archive-url=https://web.archive.org/web/20171215001435/https://en.oxforddictionaries.com/definition/us/markov_chain |url-status=dead |archive-date=15 December 2017 |title=Markov chain {{!}} Definition of Markov chain in US English by Oxford Dictionaries |website=Oxford Dictionaries {{!}} English |access-date=2017-12-14}}</ref><ref>[https://brilliant.org/wiki/markov-chains/ Definition at Brilliant.org "Brilliant Math and Science Wiki"]. Retrieved 12 May 2019</ref>}}

{{anchor|markov decision process}}{{term|[[Markov decision process]] (MDP)}}
{{defn|A [[discrete time]] [[stochastic]] [[Optimal control theory|control]] process. It provides a mathematical framework for modeling [[decision making]] in situations where outcomes are partly [[Randomness#In mathematics|random]] and partly under the control of a decision maker. MDPs are useful for studying [[optimization problem]]s solved via [[dynamic programming]] and {{gli|reinforcement learning}}.}}

{{term|[[mathematical optimization]]}}
{{ghat|Also '''mathematical programming'''.}}
{{defn|In [[mathematics]], [[computer science]], and [[operations research]], the selection of a best element (with regard to some criterion) from some set of available alternatives.<ref>"[https://glossary.computing.society.informs.org/index.php?page=nature.html The Nature of Mathematical Programming] {{webarchive|url=https://web.archive.org/web/20140305080324/https://glossary.computing.society.informs.org/index.php?page=nature.html |date=2014-03-05 }}," ''Mathematical Programming Glossary'', INFORMS Computing Society.</ref>}}

{{anchor|machine learning}}{{term|[[machine learning]] (ML)}}
{{defn|The [[Branches of science|scientific study]] of {{gli|algorithm|algorithms}} and [[statistical model]]s that [[computer system]]s use in order to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead.}}

{{term|[[machine listening]]}}
{{ghat|Also '''computer audition''' ('''CA''').}}
{{defn|A general field of study of {{gli|algorithm|algorithms}} and systems for audio understanding by machine.<ref>{{Cite book |last1=Wang |first1=Wenwu |url=https://igi-global.com/book/machine-audition-principles-algorithms-systems/40288 |title=Machine Audition: Principles, Algorithms and Systems |date=1 July 2010 |publisher=IGI Global |isbn=9781615209194 |via=igi-global.com}}</ref><ref>{{Cite web |url=https://epubs.surrey.ac.uk/596085/1/Wang_Preface_MA_2010.pdf |title=Machine Audition: Principles, Algorithms and Systems}}</ref>}}

{{term|[[machine perception]]}}
{{defn|The capability of a computer system to interpret data in a manner that is similar to the way humans use their senses to relate to the world around them.<ref>Malcolm Tatum (October 3, 2012). "What is Machine Perception".</ref><ref>Alexander Serov (January 29, 2013). "Subjective Reality and Strong Artificial Intelligence" (PDF).</ref><ref>{{Cite web |url=https://ccs.fau.edu/~hahn/mpcr/ |title=Machine Perception & Cognitive Robotics Laboratory |website=ccs.fau.edu |access-date=2016-06-18}}</ref>}}

{{term|[[mechanism design]]}}
{{defn|A field in [[economics]] and [[game theory]] that takes an [[engineering]] approach to designing economic mechanisms or [[Economic incentive|incentive]]s, toward desired objectives, in [[Strategy (game theory)|strategic setting]]s, where players act [[Rational choice theory|rationally]]. Because it starts at the end of the game, then goes backwards, it is also called reverse game theory. It has broad applications, from economics and politics (markets, auctions, voting procedures) to networked-systems (internet interdomain routing, sponsored search auctions).}}

{{term|[[mechatronics]]}}
{{ghat|Also '''mechatronic engineering'''.}}
{{defn|A [[multidisciplinary]] branch of engineering that focuses on the engineering of both [[Electrical engineering|electrical]] and [[mechanical engineering|mechanical system]]s, and also includes a combination of [[robotics]], [[electronics]], [[computer engineering|computer]], [[telecommunication]]s, [[systems engineering|system]]s, [[control engineering|control]], and [[Product engineering|product]] engineering.<ref>{{Cite web |url=https://mme.uwaterloo.ca/undergrad/mechatronics/prospective/prospective.html |title=What is Mechatronics Engineering? |website=Prospective Student Information |publisher=University of Waterloo |url-status=dead |archive-url=https://web.archive.org/web/20111006100431/https://mme.uwaterloo.ca/undergrad/mechatronics/prospective/prospective.html |archive-date=6 October 2011 |access-date=30 May 2011}}</ref><ref name="CZU">{{Cite web |url=https://mechatronics.tul.cz |title=Mechatronics (Bc., Ing., PhD.) |access-date=15 April 2011 |archive-date=15 August 2016 |archive-url=https://web.archive.org/web/20160815193349/https://mechatronics.tul.cz/ |url-status=dead }}</ref>}}

{{term|[[metabolic network reconstruction and simulation]]}}
{{defn|Allows for an in-depth insight into the molecular mechanisms of a particular organism. In particular, these models correlate the [[genome]] with molecular physiology.<ref name="Franke_2005">{{Cite journal |last1=Franke |last2=Siezen |first2=Teusink |year=2005 |title=Reconstructing the metabolic network of a bacterium from its genome. |journal=Trends in Microbiology |volume=13 |issue=11 |pages=550–558 |doi=10.1016/j.tim.2005.09.001 |pmid=16169729}}</ref>}}

{{term|[[metaheuristic]]}}
{{defn|In [[computer science]] and [[mathematical optimization]], a metaheuristic is a higher-level [[procedure (computer science)|procedure]] or [[Heuristic (computer science)|heuristic]] designed to find, generate, or select a heuristic (partial {{gli|search algorithm}}) that may provide a sufficiently good solution to an [[optimization problem]], especially with incomplete or imperfect information or limited computation capacity.<ref>{{Cite journal |last1=Balamurugan |first1=R. |last2=Natarajan |first2=A.M. |last3=Premalatha |first3=K. |year=2015 |title=Stellar-Mass Black Hole Optimization for Biclustering Microarray Gene Expression Data |journal=Applied Artificial Intelligence |volume=29 |issue=4 |pages=353–381 |doi=10.1080/08839514.2015.1016391|s2cid=44624424 |doi-access=free }}</ref><ref>{{Cite journal |last1=Bianchi |first1=Leonora |last2=Dorigo |first2=Marco |last3=Maria Gambardella |first3=Luca |last4=Gutjahr |first4=Walter J. |year=2009 |title=A survey on metaheuristics for stochastic combinatorial optimization |journal=Natural Computing |volume=8 |issue=2 |pages=239–287 |doi=10.1007/s11047-008-9098-4|s2cid=9141490 |url=https://doc.rero.ch/record/319945/files/11047_2008_Article_9098.pdf }}</ref> Metaheuristics sample a set of solutions which is too large to be completely sampled.}}

{{term|[[model checking]]}}
{{defn|In [[computer science]], model checking or property checking is, for a given model of a system, exhaustively and automatically checking whether this model meets a given [[formal specification|specification]]. Typically, one has hardware or software systems in mind, whereas the specification contains safety requirements such as the absence of [[Deadlock (computer science)|deadlock]]s and similar critical states that can cause the system to [[Crash (computing)|crash]]. Model checking is a technique for automatically verifying correctness properties of ''finite-state'' systems.}}

{{term|''[[modus ponens]]''}}
{{defn|In [[propositional logic]], ''modus ponens'' is a [[rule of inference]].<ref>Herbert B. Enderton, 2001, A Mathematical Introduction to Logic Second Edition Enderton:110, Harcourt Academic Press, Burlington MA, {{ISBN|978-0-12-238452-3}}.</ref> It can be summarized as "''P [[material conditional|implies]] Q'' and ''P'' is asserted to be true, therefore ''Q'' must be true."}}

{{term|''[[modus tollens]]''}}
{{defn|In [[propositional logic]], ''modus tollens'' is a [[Validity (logic)|valid]] [[argument form]] and a [[rule of inference]]. It is an application of the general truth that if a statement is true, then so is its [[contrapositive]]. The inference rule ''modus tollens'' asserts that the [[inference]] from ''P implies Q'' to ''the negation of Q implies the negation of P'' is valid.}}

{{term|[[Monte Carlo tree search]]}}
{{defn|In [[computer science]], Monte Carlo tree search (MCTS) is a [[heuristic (computer science)|heuristic]] {{gli|search algorithm}} for some kinds of [[decision process]]es.}}

{{anchor|multi-agent system}}{{term|[[multi-agent system]] (MAS)}}
{{ghat|Also '''self-organized system'''.}}
{{defn|A computerized system composed of multiple interacting {{gli|intelligent agent|intelligent agents}}. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a [[monolithic system]] to solve. Intelligence may include [[Scientific method|method]]ic, [[Function (computer science)|functional]], [[Algorithm|procedural]] approaches, [[algorithm]]ic [[search algorithm|search]] or {{gli|reinforcement learning}}.}}

{{anchor|multilayer perceptron}}{{term|[[multilayer perceptron]] (MLP)}}
{{defn|In {{gli|deep learning}}, a multilayer perceptron (MLP) is a name for a modern [[Feedforward neural network|feedforward]] {{gli|neural network}} consisting of fully connected neurons with nonlinear {{gli|activation function|activation functions}}, organized in layers, notable for being able to distinguish data that is not [[Linear separability|linearly separable]].<ref>Cybenko, G. 1989. Approximation by superpositions of a sigmoidal function ''[[Mathematics of Control, Signals, and Systems]]'', 2(4), 303–314.</ref>}}

{{term|[[multi-swarm optimization]]}}
{{defn|A variant of [[particle swarm optimization]] (PSO) based on the use of multiple sub-swarms instead of one (standard) swarm. The general approach in multi-swarm optimization is that each sub-swarm focuses on a specific region while a specific diversification method decides where and when to launch the sub-swarms. The multi-swarm framework is especially fitted for the optimization on multi-modal problems, where multiple (local) optima exist.}}

{{term|[[mutation (genetic algorithm)|mutation]]}}
{{defn|A [[genetic operator]] used to maintain [[genetic diversity]] from one generation of a population of [[genetic algorithm]] [[chromosome (genetic algorithm)|chromosome]]s to the next. It is analogous to biological [[mutation]]. Mutation alters one or more gene values in a chromosome from its initial state. In mutation, the solution may change entirely from the previous solution. Hence GA can come to a better solution by using mutation. Mutation occurs during evolution according to a user-definable mutation probability. This probability should be set low. If it is set too high, the search will turn into a primitive random search.}}

{{term|[[Mycin]]}}
{{defn|An early {{gli|backward chaining}} [[expert system]] that used {{gli|artificial intelligence}} to identify bacteria causing severe infections, such as [[bacteremia]] and [[meningitis]], and to recommend [[antibiotic]]s, with the dosage adjusted for patient's body weight – the name derived from the antibiotics themselves, as many antibiotics have the suffix "-mycin". The MYCIN system was also used for the diagnosis of blood clotting diseases.}}

{{glossaryend}}

{{Compact TOC|side=yes|center=yes|top=yes|num=yes|extlinks=yes|seealso=yes|refs=yes|nobreak=yes|}}

==N==
{{glossary}}
{{term|[[naive Bayes classifier]]}}
{{defn|In {{gli|machine learning}}, naive Bayes classifiers are a family of simple [[probabilistic classifier]]s based on applying [[Bayes' theorem]] with strong (naive) [[statistical independence|independence]] assumptions between the {{gli|feature|features}}.}}

{{term|[[naive semantics]]}}
{{defn|An approach used in computer science for [[knowledge representation|representing basic knowledge]] about a specific domain, and has been used in applications such as the representation of the meaning of natural language sentences in artificial intelligence applications. In a general setting the term has been used to refer to the use of a limited store of generally understood knowledge about a specific domain in the world, and has been applied to fields such as the knowledge based design of data schemas.<ref>"[https://portal.acm.org/citation.cfm?id=628188 Naive Semantics to Support Automated Database Design]", '''IEEE Transactions on Knowledge and Data Engineering'', Volume 14, issue 1 (January 2002) by V. C. Storey, R. C. Goldstein and
H. Ullrich</ref>}}

{{term|[[name binding]]}}
{{defn|In programming languages, name binding is the association of entities (data and/or code) with [[identifier]]s.<ref name="tkac08">{{Citation  |title=Using early binding and late binding in Automation |date=May 11, 2007 |url=https://support.microsoft.com/kb/245115 |publisher=Microsoft |access-date=May 11, 2009}}</ref> An identifier bound to an object is said to [[Reference (computer science)|reference]] that object. [[Machine language]]s have no built-in notion of identifiers, but name-object bindings as a service and notation for the programmer is implemented by programming languages. Binding is intimately connected with [[scoping]], as scope determines which names bind to which objects – at which locations in the program code ([[Scope (computer science)#Lexical scoping|lexically]]) and in which one of the possible execution paths ([[Scope (computer science)#Dynamic scoping|temporally]]). Use of an identifier <code>id</code> in a context that establishes a binding for <code>id</code> is called a binding (or defining) occurrence. In all other occurrences (e.g., in expressions, assignments, and subprogram calls), an identifier stands for what it is bound to; such occurrences are called applied occurrences.}}

{{term|[[named-entity recognition]] (NER)}}
{{ghat|Also '''entity identification''', '''entity chunking''', and '''entity extraction'''.}}
{{defn|A subtask of [[information extraction]] that seeks to locate and classify [[named entity]] mentions in [[unstructured data|unstructured text]] into pre-defined categories such as the person names, organizations, locations, [[medical classification|medical code]]s, time expressions, quantities, monetary values, percentages, etc.}}

{{term|[[named graph]]}}
{{defn|A key concept of [[Semantic Web]] architecture in which a set of [[Resource Description Framework]] statements (a [[Graph (discrete mathematics)|graph]]) are identified using a [[URI]],<ref>strictly speaking a URIRef</ref> allowing descriptions to be made of that set of statements such as context, provenance information or other such [[metadata]]. Named graphs are a simple extension of the RDF data model<ref>https://w3.org/TR/PR-rdf-syntax/ "Resource Description Framework (RDF) Model and Syntax Specification"</ref> through which graphs can be created but the model lacks an effective means of distinguishing between them once published on the [[World Wide Web|Web]] at large.}}

{{term|[[natural language generation]] (NLG)}}
{{defn|A software process that transforms structured data into plain-English content. It can be used to produce long-form content for organizations to automate custom reports, as well as produce custom content for a web or mobile application. It can also be used to generate short blurbs of text in interactive conversations (a [[chatbot]]) which might even be read out loud by a [[text-to-speech]] system.}}

{{anchor|natural language processing}}{{term|[[natural language processing]] (NLP)}}
{{defn|A subfield of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of [[natural language]] data.}}

{{term|[[natural language programming]]}}
{{defn|An [[ontology (information science)|ontology]]-assisted way of [[programming language|programming]] in terms of [[natural-language]] sentences, e.g. [[English language|English]].<ref>Miller, Lance A. "Natural language programming: Styles, strategies, and contrasts." IBM Systems Journal 20.2 (1981): 184–215.</ref>}}

{{term|[[network motif]]}}
{{defn|All networks, including biological networks, social networks, technological networks (e.g., computer networks and electrical circuits) and more, can be represented as [[complex network|graph]]s, which include a wide variety of subgraphs. One important local property of networks are so-called network motifs, which are defined as recurrent and [[statistically significant]] sub-graphs or patterns.}}

{{term|[[neural machine translation]] (NMT)}}
{{defn|An approach to [[machine translation]] that uses a large {{gli|artificial neural network}} to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.}}

{{anchor|artificial neural network}}
{{anchor|neural network}}
{{term|[[Neural network (machine learning)|neural network]]}}
{{defn|A '''neural network''' can refer to either a [[neural circuit]] of biological [[neuron]]s (sometimes also called a ''biological neural network''), {{em|or}} a network of [[artificial neuron]]s or [[node (computer science)|node]]s in the case of an artificial neural network.<ref>{{cite journal |first=J. J. |last=Hopfield |title=Neural networks and physical systems with emergent collective computational abilities |journal=Proc. Natl. Acad. Sci. U.S.A. |volume=79 |issue= 8|pages=2554–2558 |year=1982 |doi=10.1073/pnas.79.8.2554 |pmc=346238 |pmid=6953413|bibcode=1982PNAS...79.2554H |doi-access=free }}</ref> Artificial neural networks are used for solving {{gli|artificial intelligence}} (AI) problems; they model connections of biological neurons as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred to as a [[linear combination]]. Finally, an {{gli|activation function}} controls the [[amplitude]] of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be −1 and 1.}}

{{term|[[neural Turing machine]] (NTM)}}
{{defn|A [[recurrent neural network]] model. NTMs combine the fuzzy [[pattern matching]] capabilities of {{gli|neural network|neural networks}} with the [[algorithm]]ic power of [[programmable computer]]s. An NTM has a neural network controller coupled to [[Auxiliary memory|external memory]] resources, which it interacts with through attentional mechanisms. The memory interactions are differentiable end-to-end, making it possible to optimize them using [[gradient descent]].<ref name="MyUser_Https:_May_17_2016c">{{Cite web |url=https://linkedin.com/pulse/deep-minds-interview-googles-alex-graves-koray-sophie-curtis |title=Deep Minds: An Interview with Google's Alex Graves & Koray Kavukcuoglu |access-date=May 17, 2016}}</ref> An NTM with a [[long short-term memory]] (LSTM) network controller can infer simple algorithms such as copying, sorting, and associative recall from examples alone.<ref>{{Cite arXiv|eprint = 1410.5401|last1 = Graves|first1 = Alex|last2 = Wayne|first2 = Greg|last3 = Danihelka|first3 = Ivo|title = Neural Turing Machines|year = 2014|class = cs.NE}}</ref>}}

{{term|[[neuro-fuzzy]]}}
{{defn|Combinations of {{gli|artificial neural network|artificial neural networks}} and {{gli|fuzzy logic}}.}}

{{term|[[neurocybernetics]]}}
{{ghat|Also '''brain–computer interface''' ('''BCI'''), '''neural-control interface''' ('''NCI'''), '''mind-machine interface''' ('''MMI'''), '''direct neural interface''' ('''DNI'''), or '''brain–machine interface''' ('''BMI''').}}
{{defn|A direct communication pathway between an enhanced or wired [[brain]] and an external device. BCI differs from [[Neuromodulation (medicine)|neuromodulation]] in that it allows for bidirectional information flow. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions.<ref name="Krucoff 584">{{Cite journal |last1=Krucoff |first1=Max O. |last2=Rahimpour |first2=Shervin |last3=Slutzky |first3=Marc W. |last4=Edgerton |first4=V. Reggie |last5=Turner |first5=Dennis A. |date=2016-01-01 |title=Enhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation |journal=Frontiers in Neuroscience |volume=10 |pages=584 |doi=10.3389/fnins.2016.00584 |pmc=5186786 |pmid=28082858|doi-access=free }}</ref>}}

{{term|[[neuromorphic engineering]]}}
{{ghat|Also '''neuromorphic computing'''.}}
{{defn|A concept describing the use of [[very-large-scale integration]] (VLSI) systems containing electronic [[analog circuit]]s to mimic neuro-biological architectures present in the nervous system.<ref>{{Cite journal |last1=Mead |first1=Carver |year=1990 |title=Neuromorphic electronic systems |url=https://authors.library.caltech.edu/53090/1/00058356.pdf |journal=Proceedings of the IEEE |volume=78 |issue=10 |pages=1629–1636 |citeseerx=10.1.1.161.9762 |doi=10.1109/5.58356|s2cid=1169506 }}</ref> In recent times, the term ''neuromorphic'' has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of [[neural system]]s (for [[perception]], [[motor control]], or [[multisensory integration]]). The implementation of neuromorphic computing on the hardware level can be realized by oxide-based [[memristor]]s,<ref name="Maan 1–13">{{Cite journal |last1=Maan |first1=A. K. |last2=Jayadevi |first2=D. A. |last3=James |first3=A. P. |date=2016-01-01 |title=A Survey of Memristive Threshold Logic Circuits |journal=IEEE Transactions on Neural Networks and Learning Systems |volume=PP |issue=99 |pages=1734–1746 |arxiv=1604.07121 |doi=10.1109/TNNLS.2016.2547842 |issn=2162-237X |pmid=27164608|s2cid=1798273 }}</ref> spintronic memories,<ref>"[https://academia.edu/37832670/A_Survey_of_Spintronic_Architectures_for_Processing-in-Memory_and_Neural_Networks  A Survey of Spintronic Architectures for Processing-in-Memory and Neural Networks]", JSA, 2018</ref> threshold switches, and [[transistor]]s.<ref>{{Cite journal |last1=Zhou |first1=You |last2=Ramanathan |first2=S. |date=2015-08-01 |title=Mott Memory and Neuromorphic Devices |journal=Proceedings of the IEEE |volume=103 |issue=8 |pages=1289–1310 |doi=10.1109/JPROC.2015.2431914 |s2cid=11347598 |issn=0018-9219|url=https://zenodo.org/record/895565 }}</ref><ref>{{Cite journal |last1=Monroe |first1=D. |year=2014 |title=Neuromorphic computing gets ready for the (really) big time |journal=[[Communications of the ACM]] |volume=57 |issue=6 |pages=13–15 |doi=10.1145/2601069|s2cid=20051102 }}</ref><ref>{{Cite journal |last1=Zhao |first1=W. S. |last2=Agnus |first2=G. |last3=Derycke |first3=V. |last4=Filoramo |first4=A. |last5=Bourgoin |first5=J. -P. |last6=Gamrat |first6=C. |year=2010 |title=Nanotube devices based crossbar architecture: Toward neuromorphic computing |url=https://zenodo.org/record/3428659 |journal=Nanotechnology |volume=21 |issue=17 |pages=175202 |bibcode=2010Nanot..21q5202Z |doi=10.1088/0957-4484/21/17/175202 |pmid=20368686 |s2cid=16253700 |access-date=2 December 2019 |archive-date=10 April 2021 |archive-url=https://web.archive.org/web/20210410124706/https://zenodo.org/record/3428659 |url-status=dead }}</ref><ref name="humanbrainproject">{{YouTube|id=6RoiZ90mGfw|title=The Human Brain Project SP 9: Neuromorphic Computing Platform}}</ref>}}

{{term|[[node (computer science)|node]]}}
{{defn|A basic unit of a [[data structure]], such as a [[linked list]] or [[Tree (data structure)|tree]] data structure. Nodes contain [[data]] and also may link to other nodes. Links between nodes are often implemented by [[Pointer (computer programming)|pointer]]s.}}

{{term|[[nondeterministic algorithm]]}}
{{defn|An {{gli|algorithm}} that, even for the same input, can exhibit different behaviors on different runs, as opposed to a [[deterministic algorithm]].}}

{{term|[[nouvelle AI]]}}
{{defn|Nouvelle AI differs from {{gli|artificial intelligence|classical AI}} by aiming to produce robots with intelligence levels similar to insects. Researchers believe that intelligence can emerge organically from simple behaviors as these intelligences interacted with the "real world", instead of using the constructed worlds which symbolic AIs typically needed to have programmed into them.<ref>{{Cite web |url=https://alanturing.net/turing_archive/pages/reference%20articles/what_is_AI/What%20is%20AI11.html |title=What is Artificial Intelligence? |last1=Copeland |first1=Jack |date=May 2000 |website=AlanTuring.net |access-date=7 November 2015 |archive-date=9 November 2015 |archive-url=https://web.archive.org/web/20151109070037/http://www.alanturing.net/turing_archive/pages/reference%20articles/What_is_AI/What%20is%20AI11.html |url-status=dead }}</ref>}}

{{term|[[NP (complexity)|NP]]}}
{{defn|In {{gli|computational complexity theory}}, NP (nondeterministic polynomial time) is a [[complexity class]] used to classify [[decision problem]]s. NP is the [[Set (mathematics)|set]] of decision problems for which the [[Computational complexity theory#Problem instances|problem instance]]s, where the answer is "yes", have [[mathematical proof|proof]]s verifiable in [[polynomial time]].<ref name="kleinberg2006">{{Cite book |last1=Kleinberg |first1=Jon |url=https://archive.org/details/algorithmdesign0000klei |title=Algorithm Design |last2=Tardos |first2=Éva |publisher=Addison-Wesley |year=2006 |isbn=0-321-37291-3 |edition=2nd |page=[https://archive.org/details/algorithmdesign0000klei/page/464 464] |url-access=registration}}</ref><ref group="Note">''polynomial time'' refers to how quickly the number of operations needed by an algorithm, relative to the size of the problem, grows. It is therefore a measure of efficiency of an algorithm.</ref>}}

{{term|[[NP-completeness]]}}
{{defn|In {{gli|computational complexity theory}}, a problem is NP-complete when it can be solved by a restricted class of [[brute force search]] algorithms and it can be used to simulate any other problem with a similar algorithm. More precisely, each input to the problem should be associated with a set of solutions of polynomial length, whose validity can be tested quickly (in [[polynomial time]]<ref>{{Cite book |last1=Cobham |first1=Alan |title=Proc. Logic, Methodology, and Philosophy of Science II |publisher=North Holland |year=1965 |chapter=The intrinsic computational difficulty of functions |author-link=Alan Cobham}}</ref>), such that the output for any input is "yes" if the solution set is non-empty and "no" if it is empty.}}

{{term|[[NP-hardness]]}}
{{ghat|Also '''[[NP (complexity)|non-deterministic polynomial-time]] hardness'''.}}
{{defn|In {{gli|computational complexity theory}}, the defining property of a class of problems that are, informally, "at least as hard as the hardest problems in NP". A simple example of an NP-hard problem is the [[subset sum problem]].}}
{{glossaryend}}

{{Compact TOC|side=yes|center=yes|top=yes|num=yes|extlinks=yes|seealso=yes|refs=yes|nobreak=yes|}}

==O==
{{glossary}}
{{term|[[Occam's razor]]}}
{{ghat|Also '''Ockham's razor''' or '''Ocham's razor'''.}}
{{defn|The problem-solving principle that states that when presented with competing [[hypotheses]] that make the same predictions, one should select the solution with the fewest assumptions;<ref>{{Cite web |url=https://math.ucr.edu/home/baez/physics/General/occam.html |title=What is Occam's Razor? |website=math.ucr.edu |access-date=2019-06-01}}</ref> the principle is not meant to filter out hypotheses that make different predictions. The idea is attributed to the English [[Franciscan]] friar [[William of Ockham]] ({{c.}} 1287–1347), a [[Scholasticism|scholastic]] philosopher and [[Catholic theology|theologian]].}}

{{term|[[offline learning]]}}
{{defn|A {{gli|machine learning}} training approach in which a model is trained on a fixed dataset that is not updated during the learning process.}}

{{term|[[online machine learning]]}}
{{defn|A method of {{gli|machine learning}} in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once. Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of [[out-of-core]] algorithms. It is also used in situations where it is necessary for the algorithm to dynamically adapt to new patterns in the data, or when the data itself is generated as a function of time.}}

{{term|[[ontology learning]]}}
{{ghat|Also '''ontology extraction''', '''ontology generation''', or '''ontology acquisition'''.}}
{{defn|The automatic or semi-automatic creation of [[ontologies]], including extracting the corresponding [[Domain of discourse|domain's]] terms and the relationships between the [[Conceptualization (information science)|concept]]s that these terms represent from a [[Text corpus|corpus]] of natural language text, and encoding them with an [[ontology language]] for easy retrieval.}}

{{term|[[OpenAI]]}}
{{defn|The for-profit corporation OpenAI LP, whose [[parent company|parent organization]] is the non-profit organization OpenAI Inc<ref>"OpenAI shifts from nonprofit to 'capped-profit' to attract capital". TechCrunch. Retrieved 2019-05-10.</ref> that conducts research in the field of {{gli|artificial intelligence}} (AI) with the stated aim to promote and develop {{gli|friendly artificial intelligence|friendly AI}} in such a way as to benefit humanity as a whole.}}

{{term|[[OpenCog]]}}
{{defn|A project that aims to build an {{gli|open-source software|open-source}} artificial intelligence framework. OpenCog Prime is an architecture for robot and virtual [[embodied cognition]] that defines a set of interacting components designed to give rise to human-equivalent {{gli|artificial general intelligence}} (AGI) as an [[emergent phenomenon]] of the whole system.<ref>{{Cite web |url=https://cybertechnews.org/?p=915 |title=OpenCog: Open-Source Artificial General Intelligence for Virtual Worlds |website=CyberTech News |date=2009-03-06 |url-status=dead |archive-url=https://web.archive.org/web/20090306053354/https://cybertechnews.org/?p=915 |archive-date=2009-03-06 |access-date=2016-10-01}}</ref>}}

{{term|[[Open Mind Common Sense]]}}
{{defn|An artificial intelligence project based at the [[Massachusetts Institute of Technology]] (MIT) [[MIT Media Lab|Media Lab]] whose goal is to build and utilize a large [[commonsense knowledge base]] from the contributions of many thousands of people across the Web.}}

{{anchor|open-source software}}{{term|[[open-source software]] (OSS)}}
{{defn|A type of [[computer software]] in which [[source code]] is released under an [[Open-source license|license]] in which the [[copyright]] holder grants users the rights to study, change, and [[Software distribution|distribute the software]] to anyone and for any purpose.<ref>{{Cite book |last1=St. Laurent |first1=Andrew M. |url=https://books.google.com/books?id=04jG7TTLujoC&pg=PA4 |title=Understanding Open Source and Free Software Licensing |publisher=O'Reilly Media |year=2008 |isbn=9780596553951 |page=4}}</ref> Open-source [[software]] may be developed in an [[Open-source model|collaborative public manner]]. Open-source software is a prominent example of [[open collaboration]].<ref name="Open Collaboration">{{Cite journal |last1=Levine |first1=Sheen S. |last2=Prietula |first2=Michael J. |date=2013-12-30 |title=Open Collaboration for Innovation: Principles and Performance |journal=Organization Science |volume=25 |issue=5 |pages=1414–1433 |arxiv=1406.7541 |doi=10.1287/orsc.2013.0872 |s2cid=6583883 |issn=1047-7039}}</ref>}}

{{anchor|underfitting}}{{term|[[overfitting]]}}
{{defn|"The production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit to additional data or predict future observations reliably".<ref>Definition of "[https://web.archive.org/web/20171107014257/https://en.oxforddictionaries.com/definition/overfitting overfitting]" at [[OxfordDictionaries.com]]: this definition is specifically for statistics.</ref> In other words, an overfitted model memorizes training data details but cannot {{gli|generalization|generalize}} to new data. Conversely, an '''underfitted''' model is too simple to capture the complexity of the training data.}}
{{glossaryend}}

==P==
{{glossary}}
{{term|[[partial order reduction]]}}
{{defn|A technique for reducing the size of the [[State transition system|state-space]] to be searched by a [[model checking]] or [[automated planning and scheduling]] [[algorithm]]. It exploits the commutativity of concurrently executed [[Transition (computer science)|transition]]s, which result in the same state when executed in different orders.}}

{{term|[[partially observable Markov decision process]] (POMDP)}}
{{defn|A generalization of a {{gli|markov decision process|Markov decision process}} (MDP). A POMDP models an agent decision process in which it is assumed that the system dynamics are determined by an MDP, but the agent cannot directly observe the underlying state. Instead, it must maintain a probability distribution over the set of possible states, based on a set of observations and observation probabilities, and the underlying MDP.}}

{{term|[[particle swarm optimization]] (PSO)}}
{{defn|A computational method that [[Mathematical optimization|optimizes]] a problem by [[iterative method|iteratively]] trying to improve a [[candidate solution]] with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed [[Point particle|particle]]s, and moving these particles around in the [[Optimization (mathematics)#Concepts and notation|search-space]] according to simple [[formula|mathematical formulae]] over the particle's position and velocity. Each particle's movement is influenced by its local best known position, but is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions.}}

{{term|[[pathfinding]]}}
{{ghat|Also '''pathing'''.}}
{{defn|The plotting, by a computer application, of the shortest route between two points. It is a more practical variant on [[Maze#Solving mazes|solving maze]]s. This field of research is based heavily on {{gli|Dijkstra's algorithm}} for finding a shortest path on a [[Glossary of graph theory#Weighted graphs and networks|weighted graph]].}}

{{term|[[pattern recognition]]}}
{{defn|Concerned with the automatic discovery of regularities in data through the use of computer algorithms and with the use of these regularities to take actions such as classifying the data into different categories.<ref>Bishop, Christopher M. (2006). Pattern Recognition and Machine Learning (PDF). Springer. p. vii. Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years.</ref>}}

{{term|[[perceptron]]}}
{{defn|An {{gli|algorithm}} for {{gli|supervised learning}} of binary {{gli|classification|classifiers}}.}}

{{term|[[predicate logic]]}}
{{ghat|Also '''first-order logic''', '''predicate logic''', and '''first-order predicate calculus'''.}}
{{defn|A collection of [[formal system]]s used in [[mathematics]], [[philosophy]], [[linguistics]], and [[computer science]]. First-order logic uses [[Quantification (logic)|quantified variable]]s over non-logical objects and allows the use of sentences that contain variables, so that rather than propositions such as ''Socrates is a man'' one can have expressions in the form "there exists x such that x is Socrates and x is a man" and ''there exists'' is a quantifier while ''x'' is a variable.<ref name="auto" /> This distinguishes it from [[propositional logic]], which does not use quantifiers or [[finitary relation|relation]]s;<ref>[[George Edward Hughes|Hughes, G. E.]], & [[Max Cresswell|Cresswell, M. J.]], ''[https://books.google.com/books?id=Dsn1xWNB4MEC&q=%22first-order+logic%22 A New Introduction to Modal Logic]'' ([[London]]: [[Routledge]], 1996), [https://books.google.com/books?id=_CB5wiBeaA4C&pg=PA161 p.161].</ref> in this sense, propositional logic is the foundation of first-order logic.}}

{{term|[[predictive analytics]]}}
{{defn|A variety of statistical techniques from [[data mining]], [[predictive modelling]], and {{gli|machine learning}}, that analyze current and historical facts to make predictions about future or otherwise unknown events.<ref name="Nyce">{{Citation |last1=Nyce |first1=Charles |title=Predictive Analytics White Paper |url=https://the-digital-insurer.com/wp-content/uploads/2013/12/78-Predictive-Modeling-White-Paper.pdf |page=1 |year=2007 |publisher=American Institute for Chartered Property Casualty Underwriters/Insurance Institute of America}}</ref><ref name="Eckerson">{{Citation |last1=Eckerson |first1=Wayne |title=Extending the Value of Your Data Warehousing Investment |date=May 10, 2007 |url=https://tdwi.org/articles/2007/05/10/predictive-analytics.aspx?sc_lang=en |publisher=The Data Warehouse Institute}}</ref>}}

{{term|[[principal component analysis]] (PCA)}}
{{defn|A statistical procedure that uses an [[orthogonal transformation]] to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of [[Correlation and dependence|linearly uncorrelated]] variables called principal components. This transformation is defined in such a way that the first principal component has the largest possible [[variance]] (that is, accounts for as much of the variability in the data as possible), and each succeeding component, in turn, has the highest variance possible under the constraint that it is [[orthogonal]] to the preceding components. The resulting vectors (each being a [[linear combination]] of the variables and containing ''n'' observations) are an uncorrelated [[orthogonal basis set]]. PCA is sensitive to the relative scaling of the original variables.}}

{{term|[[principle of rationality]]}}
{{ghat|Also '''rationality principle'''.}}
{{defn|A principle coined by [[Karl R. Popper]] in his Harvard Lecture of 1963, and published in his book ''Myth of Framework''.<ref>[[Karl R. Popper]], ''The Myth of Framework'', London (Routledge) 1994, chap. 8.</ref> It is related to what he called the 'logic of the situation' in an ''Economica'' article of 1944/1945, published later in his book ''The Poverty of Historicism''.<ref>[[Karl R. Popper]], ''The Poverty of Historicism'', London (Routledge) 1960, chap. iv, sect. 31.</ref> According to Popper's rationality principle, agents act in the most adequate way according to the objective situation. It is an idealized conception of human behavior which he used to drive his model of [[situational logic]].}}

{{term|[[probabilistic programming]] (PP)}}
{{defn|A [[programming paradigm]] in which [[probabilistic model]]s are specified and inference for these models is performed automatically.<ref name="physorg">{{Cite news |url=https://phys.org/news/2015-04-probabilistic-lines-code-thousands.html |title=Probabilistic programming does in 50 lines of code what used to take thousands |date=April 13, 2015 |work=phys.org |access-date=2015-04-13}}</ref> It represents an attempt to unify probabilistic modeling and traditional general-purpose programming in order to make the former easier and more widely applicable.<ref>{{Cite web |url=https://probabilistic-programming.org/wiki/Home |title=Probabilistic Programming |website=probabilistic-programming.org |url-status=dead |archive-url=https://web.archive.org/web/20160110035042/https://probabilistic-programming.org/wiki/Home |archive-date=10 January 2016 |access-date=31 July 2019}}</ref><ref name="Pfeffer2014">Pfeffer, Avrom (2014), ''Practical Probabilistic Programming'', Manning Publications. p.28. {{ISBN|978-1 6172-9233-0}}</ref> It can be used to create systems that help make decisions in the face of uncertainty. Programming languages used for probabilistic programming are referred to as "Probabilistic programming languages" (PPLs).}}

{{anchor|production system}}{{term|[[Production system (computer science)|production system]]}}
{{defn|A computer program typically used to provide some form of AI, which consists primarily of a set of rules about behavior, but also includes the mechanism necessary to follow those rules as the system responds to states of the world.}}

{{term|[[programming language]]}}
{{defn|A {{gli|formal language}}, which comprises a [[Instruction set|set of instruction]]s that produce various kinds of [[Input/output|output]]. Programming languages are used in [[computer programming]] to implement {{gli|algorithm|algorithms}}.}}

{{term|[[Prolog]]}}
{{defn|A {{gli|logic programming}} language associated with artificial intelligence and [[computational linguistics]].<ref name="Clocksin2003">{{Cite book |last1=Clocksin |first1=William F. |title=Programming in Prolog |last2=Mellish |first2=Christopher S. |publisher=Springer-Verlag |year=2003 |isbn=978-3-540-00678-7 |location=Berlin; New York}}</ref><ref name="Bratko2012">{{Cite book |last1=Bratko |first1=Ivan |title=Prolog programming for artificial intelligence |publisher=Addison Wesley |year=2012 |isbn=978-0-321-41746-6 |edition=4th |location=Harlow, England; New York}}</ref><ref name="Covington1994">{{Cite book |last1=Covington |first1=Michael A. |title=Natural language processing for Prolog programmers |publisher=Prentice Hall |year=1994 |isbn=978-0-13-629213-5 |location=Englewood Cliffs, N.J.}}</ref>  Prolog has its roots in [[first-order logic]], a [[formal logic]], and unlike many other [[programming language]]s, Prolog is intended primarily as a [[declarative programming|declarative]] programming language: the program logic is expressed in terms of relations, represented as facts and [[Rule of inference|rule]]s. A computation is initiated by running a ''query'' over these relations.<ref>Lloyd, J. W. (1984). Foundations of logic programming. Berlin: Springer-Verlag. {{ISBN|978-3-540-13299-8}}.</ref>}}

{{term|[[propositional calculus]]}}
{{ghat|Also '''propositional logic''', '''statement logic''', '''sentential calculus''', '''sentential logic''', and '''[[zeroth-order logic]]'''.}}
{{defn|A branch of [[logic]] which deals with [[proposition]]s (which can be true or false) and argument flow. Compound propositions are formed by connecting propositions by [[logical connective]]s. The propositions without logical connectives are called atomic propositions. Unlike [[first-order logic]], propositional logic does not deal with non-logical objects, predicates about them, or quantifiers. However, all the machinery of propositional logic is included in first-order logic and higher-order logics. In this sense, propositional logic is the foundation of first-order logic and higher-order logic.}}

{{anchor|proximal policy optimization}}{{term|[[proximal policy optimization]] (PPO)}}
{{defn|A {{gli|reinforcement learning}} {{gli|algorithm}} for training an {{gli|intelligent agent}}'s decision function to accomplish difficult tasks.}}

{{term|[[Python (programming language)|Python]]}}
{{defn|An [[interpreted language|interpreted]], [[high-level programming language|high-level]], [[general-purpose programming language|general-purpose]] {{gli|programming language}} created by [[Guido van Rossum]] and first released in 1991. Python's design philosophy emphasizes [[code readability]] with its notable use of [[significant whitespace]]. Its language constructs and [[object-oriented]] approach aim to help programmers write clear, logical code for small and large-scale projects.<ref>Kuhlman, Dave. "A Python Book: Beginning Python, Advanced Python, and Python Exercises". Section 1.1. Archived from the original (PDF) on 23 June 2012.</ref>}}

{{term|[[PyTorch]]}}
{{defn|A {{gli|machine learning}} [[Library (computing)|library]] based on the [[Torch (machine learning)|Torch]] library,<ref>{{cite news|url=https://www.infoworld.com/article/3159120/artificial-intelligence/facebook-brings-gpu-powered-machine-learning-to-python.html|title=Facebook brings GPU-powered machine learning to Python|last=Yegulalp|first=Serdar|date=19 January 2017|work=InfoWorld|access-date=11 December 2017}}</ref><ref>{{cite web|url=https://www.oreilly.com/ideas/why-ai-and-machine-learning-researchers-are-beginning-to-embrace-pytorch|title=Why AI and machine learning researchers are beginning to embrace PyTorch|last=Lorica|first=Ben|date=3 August 2017|publisher=O'Reilly Media|access-date=11 December 2017}}</ref><ref>{{Cite book|title=Deep Learning with Python|last=Ketkar|first=Nikhil|date=2017|publisher=Apress, Berkeley, CA|isbn=9781484227657|pages=195–208|language=en|doi=10.1007/978-1-4842-2766-4_12|chapter=Introduction to PyTorch}}</ref> used for applications such as {{gli|computer vision}} and {{gli|natural language processing}},<ref>{{Cite web|url=https://www.datacamp.com/tutorial/nlp-with-pytorch-a-comprehensive-guide|title=NLP with PyTorch: A Comprehensive Guide|author=Moez Ali|date=Jun 2023|website=datacamp.com|language=en|access-date=2024-04-01}}</ref> originally developed by [[Meta AI]] and now part of the [[Linux Foundation]] umbrella.<ref>{{Cite news|url=https://www.oreilly.com/ideas/when-two-trends-fuse-pytorch-and-recommender-systems|title=When two trends fuse: PyTorch and recommender systems|last=Patel|first=Mo|date=2017-12-07|work=O'Reilly Media|access-date=2017-12-18|language=en}}</ref><ref>{{Cite news|url=https://techcrunch.com/2017/09/07/facebook-and-microsoft-collaborate-to-simplify-conversions-from-pytorch-to-caffe2/|title=Facebook and Microsoft collaborate to simplify conversions from PyTorch to Caffe2|last=Mannes|first=John|work=[[TechCrunch]]|access-date=2017-12-18|language=en|quote=FAIR is accustomed to working with PyTorch – a deep learning framework optimized for achieving state of the art results in research, regardless of resource constraints. Unfortunately in the real world, most of us are limited by the computational capabilities of our smartphones and computers.}}</ref><ref>{{Cite web|url=https://venturebeat.com/2017/11/29/tech-giants-are-using-open-source-frameworks-to-dominate-the-ai-community/|title=Tech giants are using open source frameworks to dominate the AI community|last=Arakelyan|first=Sophia|date=2017-11-29|website=[[VentureBeat]]|language=en-US|access-date=2017-12-18}}</ref><ref>{{Cite web |title=PyTorch strengthens its governance by joining the Linux Foundation |url=https://pytorch.org/blog/PyTorchfoundation/ |access-date=2022-09-13 |website=pytorch.org |language=en}}</ref>}}
{{glossaryend}}

{{Compact TOC|side=yes|center=yes|top=yes|num=yes|extlinks=yes|seealso=yes|refs=yes|nobreak=yes|}}

==Q==
{{glossary}}
{{term|[[Q-learning]]}}
{{defn|A [[Model-free (reinforcement learning)|model-free]] {{gli|reinforcement learning}} {{gli|algorithm}} for learning the value of an action in a particular state.}}

{{term|[[qualification problem]]}}
{{defn|In philosophy and artificial intelligence (especially {{gli|knowledge-based system|knowledge-based systems}}), the qualification problem is concerned with the impossibility of listing ''all'' of the [[precondition]]s required for a real-world action to have its intended effect.<ref name="reiter">{{Cite book |last1=Reiter |first1=Raymond |title=Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems |url=https://archive.org/details/knowledgeactionl00reit_022 |url-access=limited |publisher=The MIT Press |year=2001 |isbn=9780262527002 |location=Cambridge, Massachusetts |pages=[https://archive.org/details/knowledgeactionl00reit_022/page/n40 20]–22}}</ref><ref>{{Cite journal |last1=Thielscher |first1=Michael |date=September 2001 |title=The Qualification Problem: A solution to the problem of anomalous models |journal=Artificial Intelligence |volume=131 |issue=1–2 |pages=1–37 |doi=10.1016/S0004-3702(01)00131-X}}</ref> It might be posed as ''how to deal with the things that prevent me from achieving my intended result''. It is strongly connected to, and opposite the [[ramification problem|ramification side]] of, the [[frame problem]].<ref name="reiter" />}}

{{term|[[quantifier (logic)|quantifier]]}}
{{defn|In [[logic]], quantification specifies the quantity of specimens in the [[domain of discourse]] that satisfy an [[open formula]]. The two most common quantifiers mean "[[for all]]" and "[[there exist]]s". For example, in arithmetic, quantifiers allow one to say that the natural numbers go on forever, by writing that ''for all'' n (where n is a natural number), there is another number (say, the successor of n) which is one bigger than n.<!--this is to answer {{Technical|date=March 2016}} by making at least the first paragraph somewhat approachable. Of course the rest must be technical.-->}}

{{term|[[quantum computing]]}}
{{defn|The use of [[quantum-mechanical]] [[phenomena]] such as [[quantum superposition|superposition]] and [[quantum entanglement|entanglement]] to perform [[computation]]. A quantum computer is used to perform such computation, which can be implemented theoretically or physically.<ref name="2018Report">{{Cite book |title=Quantum Computing : Progress and Prospects (2018) |publisher=National Academies Press |year=2019 |isbn=978-0-309-47969-1 |editor-last=Grumbling |editor-first=Emily |location=Washington, DC |page=I-5 |doi=10.17226/25196 |s2cid=125635007 |oclc=1081001288 |editor-last2=Horowitz |editor-first2=Mark}}</ref>{{rp|I-5}}}}

{{term|[[query language]]}}
{{defn|Query languages or data query languages (DQLs) are [[computer language]]s used to make queries in [[database]]s and [[information system]]s. Broadly, query languages can be classified according to whether they are database query languages or [[information retrieval query language]]s. The difference is that a database query language attempts to give factual answers to factual questions, while an information retrieval query language attempts to find documents containing information that is relevant to an area of inquiry.}}
{{glossaryend}}

==R==
{{glossary}}
{{term|[[R programming language]]}}
{{defn|A {{gli|programming language}} and [[free software]] environment for [[statistical computing]] and graphics supported by the R Foundation for Statistical Computing.{{refn | R language and environment
{{Cite web |url=https://cran.r-project.org/doc/FAQ/R-FAQ.html#What-is-R_003f |title=R FAQ |last1=Hornik |first1=Kurt |date=2017-10-04 |website=The Comprehensive R Archive Network |at=2.1 What is R? |access-date=2018-08-06}}
R Foundation
{{Cite web |url=https://cran.r-project.org/doc/FAQ/R-FAQ.html#What-is-the-R-Foundation_003f |title=R FAQ |last1=Hornik |first1=Kurt |date=2017-10-04 |website=The Comprehensive R Archive Network |at=2.13 What is the R Foundation? |access-date=2018-08-06}}
The R Core Team asks authors who use R in their data analysis to cite the software using:
R Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://R-project.org/.
}} The R language is widely used among [[statistician]]s and [[data miner]]s for developing [[statistical software]]{{refn | widely used
{{Cite web |last1=Fox |first1=John |last2=Andersen |first2=Robert |name-list-style=amp |date=January 2005 |title=Using the R Statistical Computing Environment to Teach Social Statistics Courses |url=https://socialsciences.mcmaster.ca/jfox/Teaching-with-R.pdf |publisher=Department of Sociology, McMaster University |access-date=2018-08-06}}
{{Cite news |last1=Vance |first1=Ashlee |author-link=Ashlee Vance |url=https://nytimes.com/2009/01/07/technology/business-computing/07program.html |title=Data Analysts Captivated by R's Power |date=2009-01-06 |work=[[The New York Times]] |access-date=2018-08-06 |quote=R is also the name of a popular programming language used by a growing number of data analysts inside corporations and academia. It is becoming their lingua franca...}}
}} and [[data analysis]].<ref>{{Cite news |last1=Vance |first1=Ashlee |url=https://nytimes.com/2009/01/07/technology/business-computing/07program.html |title=Data Analysts Captivated by R's Power |date=2009-01-06 |work=[[The New York Times]] |access-date=2018-08-06 |quote=R is also the name of a popular programming language used by a growing number of data analysts inside corporations and academia. It is becoming their lingua franca...}}</ref>}}

{{term|[[radial basis function network]]}}
{{defn|In the field of [[mathematical modeling]], a radial basis function network is an {{gli|artificial neural network}} that uses [[radial basis function]]s as {{gli|activation function|activation functions}}. The output of the network is a [[linear combination]] of radial basis functions of the inputs and neuron [[parameters]]. Radial basis function networks have many uses, including [[function approximation]], [[time series prediction]], {{gli|classification}}, and system [[Control theory|control]]. They were first formulated in a 1988 paper by Broomhead and Lowe, both researchers at the [[Royal Signals and Radar Establishment]].<ref>{{Cite tech report |last1=Broomhead |first1=D. S. |last2=Lowe |first2=David |year=1988 |title=Radial basis functions, multi-variable functional interpolation and adaptive networks |institution=[[RSRE]] |number=4148 |url=https://apps.dtic.mil/sti/pdfs/ADA196234.pdf|archive-url=https://web.archive.org/web/20130409223044/https://dtic.mil/cgi-bin/GetTRDoc?AD=ADA196234|url-status=live|archive-date=9 April 2013}}</ref><ref>{{Cite journal |last1=Broomhead |first1=D. S. |last2=Lowe |first2=David |year=1988 |title=Multivariable functional interpolation and adaptive networks |url=https://sci2s.ugr.es/keel/pdf/algorithm/articulo/1988-Broomhead-CS.pdf |journal=Complex Systems |volume=2 |pages=321–355}}</ref><ref>{{Cite journal |last1=Schwenker |first1=Friedhelm |last2=Kestler |first2=Hans A. |last3=Palm |first3=Günther |year=2001 |title=Three learning phases for radial-basis-function networks |journal=Neural Networks |volume=14 |issue=4–5 |pages=439–458 |doi=10.1016/s0893-6080(01)00027-2 |pmid=11411631}}</ref>}}

{{term|[[random forest]]}}
{{ghat|Also '''random decision forest'''.}}
{{defn|An [[ensemble learning]] method for {{gli|classification}}, {{gli|regression}}, and other tasks that operates by constructing a multitude of [[decision tree learning|decision tree]]s at training time and outputting the class that is the [[mode (statistics)|mode]] of the classes (classification) or mean prediction (regression) of the individual trees.<ref>Ho, Tin Kam (1995). Random Decision Forests (PDF). Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, 14–16 August 1995. pp. 278–282. Archived from the original (PDF) on 17 April 2016. Retrieved 5 June 2016.</ref><ref>{{Cite journal |last1=Ho |first1=TK |year=1998 |title=The Random Subspace Method for Constructing Decision Forests |journal=IEEE Transactions on Pattern Analysis and Machine Intelligence |volume=20 |issue=8 |pages=832–844 |doi=10.1109/34.709601|s2cid=206420153 |url=https://repositorio.unal.edu.co/handle/unal/81834 }}</ref> Random decision forests correct for decision trees' habit of {{gli|overfitting}} to their [[training set]].<ref>Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome(2008). The Elements of Statistical Learning (2nd ed.). Springer. {{ISBN|0-387-95284-5}}.</ref>}}

{{term|[[reasoning system]]}}
{{defn|In [[information technology]] a reasoning system is a [[software system]] that generates conclusions from available [[knowledge]] using [[logic]]al techniques such as [[Deductive reasoning|deduction]] and [[Inductive reasoning|induction]]. Reasoning systems play an important role in the implementation of artificial intelligence and [[knowledge-based system]]s.}}

{{term|[[recurrent neural network]] (RNN)}}
{{defn|A class of {{gli|artificial neural network|artificial neural networks}} where connections between nodes form a [[directed graph]] along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Unlike [[feedforward neural network]]s, RNNs can use their internal state (memory) to process sequences of inputs. This makes them applicable to tasks such as unsegmented, connected [[handwriting recognition]]<ref>{{Cite journal |last1=Graves |first1=A. |last2=Liwicki |first2=M. |last3=Fernandez |first3=S. |last4=Bertolami |first4=R. |last5=Bunke |first5=H. |last6=Schmidhuber |first6=J. |author-link6=Jürgen Schmidhuber |year=2009 |title=A Novel Connectionist System for Improved Unconstrained Handwriting Recognition |url=https://idsia.ch/~juergen/tpami_2008.pdf |journal=IEEE Transactions on Pattern Analysis and Machine Intelligence |volume=31 |issue=5 |pages=855–868 |citeseerx=10.1.1.139.4502 |doi=10.1109/tpami.2008.137 |pmid=19299860|s2cid=14635907 }}</ref> or [[speech recognition]].<ref name="sak2014">{{Cite web |url=https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43905.pdf |title=Long Short-Term Memory recurrent neural network architectures for large scale acoustic modeling |last1=Sak |first1=Hasim |last2=Senior |first2=Andrew |year=2014 |url-status=dead |archive-url=https://web.archive.org/web/20180424203806/https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43905.pdf |archive-date=24 April 2018 |access-date=6 August 2019 |last3=Beaufays |first3=Francoise}}</ref><ref name="liwu2015">{{Cite arXiv |eprint=1410.4281 |class=cs.CL |first1=Xiangang |last1=Li |first2=Xihong |last2=Wu |title=Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition |date=2014-10-15}}</ref>}}

{{anchor|regression}}{{term|[[regression analysis]]}}
{{defn|A set of statistical processes for estimating the relationships between a dependent variable (often called the outcome or response variable, or ''label'' in {{gli|machine learning}}) and one or more error-free independent variables (often called regressors, predictors, covariates, explanatory variables, or {{gli|feature|features}}). The most common form of regression analysis is linear regression, in which one finds the line (or a more complex linear combination) that most closely fits the data according to a specific mathematical criterion.}}

{{anchor|regularization}}{{term|[[Regularization (mathematics)|regularization]]}}
{{defn|A set of techniques such as {{gli|dropout}}, {{gli|early stopping}}, and [[Regularization (mathematics)#L1 and L2 Regularization|L1 and L2 regularization]] to reduce {{gli|overfitting|overfitting and underfitting}} when training a learning {{gli|algorithm}}.}}

{{anchor|reinforcement learning}}{{term|[[reinforcement learning]] (RL)}}
{{defn|An area of {{gli|machine learning}} concerned with how [[software agent]]s ought to take [[Action selection|action]]s in an environment so as to maximize some notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside {{gli|supervised learning|supervised}} and {{gli|unsupervised learning}}. It differs from supervised learning in that labelled input/output pairs need not be presented, and sub-optimal actions need not be explicitly corrected. Instead the focus is finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).<ref name="kaelbling">{{Cite journal |last1=Kaelbling |first1=Leslie P. |last2=Littman |first2=Michael L. |author-link2=Michael L. Littman |last3=Moore |first3=Andrew W. |author-link3=Andrew W. Moore |year=1996 |title=Reinforcement Learning: A Survey |url=https://cs.washington.edu/research/jair/abstracts/kaelbling96a.html |url-status=dead |journal=Journal of Artificial Intelligence Research |volume=4 |pages=237–285 |arxiv=cs/9605103 |doi=10.1613/jair.301 |s2cid=1708582 |archive-url=http://webarchive.loc.gov/all/20011120234539/https://cs.washington.edu/research/jair/abstracts/kaelbling96a.html |archive-date=20 November 2001 |author-link1=Leslie P. Kaelbling |access-date=5 July 2022 }}</ref>}}

{{anchor|reinforcement learning from human feedback}}{{term|[[reinforcement learning from human feedback]] (RLHF)}}
{{defn|A technique that involve training a "reward model" to predict how humans rate the quality of generated content, and then training a {{gli|generative artificial intelligence|generative AI}} model to satisfy this reward model via {{gli|reinforcement learning}}. It can be used for example to make the generative AI model more truthful or less harmful.<ref>{{Cite web |last=Patrizio |first=Andy |title=What is reinforcement learning from human feedback (RLHF)? |url=https://www.techtarget.com/whatis/definition/reinforcement-learning-from-human-feedback-RLHF |access-date=2024-01-28 |website=TechTarget |language=en}}</ref>}}

{{term|[[Feature learning|representation learning]]}}
{{defn|See {{gli|feature learning}}.}}

{{term|[[reservoir computing]]}}
{{defn|A framework for computation that may be viewed as an extension of {{gli|neural network|neural networks}}.<ref>[[Benjamin Schrauwen|Schrauwen, Benjamin]], [[David Verstraeten]], and [[Jan Van Campenhout]]. "An overview of reservoir computing: theory, applications, and implementations." Proceedings of the European Symposium on Artificial Neural Networks ESANN 2007, pp. 471–482.</ref> Typically an input signal is fed into a fixed (random) [[dynamical system]] called a ''reservoir'' and the dynamics of the reservoir map the input to a higher dimension. Then a simple ''readout'' mechanism is trained to read the state of the reservoir and map it to the desired output. The main benefit is that training is performed only at the readout stage and the reservoir is fixed. [[Liquid-state machine]]s<ref>{{Cite journal |last1=Mass |first1=Wolfgang |author-link=Wolfgang Maas |last2=Nachtschlaeger |first2=T. |last3=Markram |first3=H. |year=2002 |title=Real-time computing without stable states: A new framework for neural computation based on perturbations |journal=Neural Computation |volume=14 |issue=11 |pages=2531–2560|doi=10.1162/089976602760407955 |pmid=12433288 |s2cid=1045112 |url=http://infoscience.epfl.ch/record/117805 }}</ref> and [[echo state network]]s<ref>[[Herbert Jaeger|Jaeger, Herbert]], "The echo state approach to analyzing and training recurrent neural networks." Technical Report 154 (2001), German National Research Center for Information Technology.</ref> are two major types of reservoir computing.<ref>{{cite journal | doi=10.4249/scholarpedia.2330 | doi-access=free | title=Echo state network | year=2007 | last1=Jaeger | first1=Herbert | journal=Scholarpedia | volume=2 | issue=9 | page=2330 | bibcode=2007SchpJ...2.2330J }}</ref>}}

{{term|[[Resource Description Framework]] (RDF)}}
{{defn|A family of [[World Wide Web Consortium]] (W3C) [[specification]]s<ref>{{Cite web |url=https://dblab.ntua.gr/~bikakis/XMLSemanticWebW3CTimeline.pdf |title=XML and Semantic Web W3C Standards Timeline |date=2012-02-04 |access-date=5 July 2022 |archive-date=6 July 2022 |archive-url=https://web.archive.org/web/20220706210815/http://www.dblab.ntua.gr/~bikakis/XMLSemanticWebW3CTimeline.pdf |url-status=dead }}</ref> originally designed as a [[metadata]] [[data model]]. It has come to be used as a general method for conceptual description or modeling of information that is implemented in [[web resource]]s, using a variety of syntax notations and [[data serialization]] formats. It is also used in [[knowledge management]] applications.}}

{{term|[[restricted Boltzmann machine]] (RBM)}}
{{defn|A [[generative model|generative]] [[stochastic neural network|stochastic]] {{gli|artificial neural network}} that can learn a [[probability distribution]] over its set of inputs.}}

{{term|[[Rete algorithm]]}}
{{defn|A [[pattern matching]] {{gli|algorithm}} for implementing [[rule-based system]]s. The algorithm was developed to efficiently apply many [[Rule of inference|rule]]s or patterns to many objects, or [[fact]]s, in a [[knowledge base]]. It is used to determine which of the system's rules should fire based on its data store, its facts.}}

{{term|[[robotics]]}}
{{defn|An interdisciplinary branch of science and engineering that includes [[mechanical engineering]], [[electronic engineering]], [[information engineering]], [[computer science]], and others. Robotics deals with the design, construction, operation, and use of [[robot]]s, as well as [[computer system]]s for their control, [[sensory feedback]], and [[Data processing|information processing]].}}

{{term|[[rule-based system]]}}
{{defn|In [[computer science]], a rule-based system is used to store and manipulate knowledge to interpret information in a useful way. It is often used in artificial intelligence applications and research. Normally, the term ''rule-based system'' is applied to systems involving human-crafted or curated rule sets. Rule-based systems constructed using automatic rule inference, such as [[rule-based machine learning]], are normally excluded from this system type.}}
{{glossaryend}}

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==S==
{{glossary}}
{{term|[[satisfiability]]}}
{{defn|In [[mathematical logic]], satisfiability and [[Validity (logic)|validity]] are elementary concepts of [[semantic]]s. A [[formula (mathematical logic)|formula]] is ''satisfiable'' if it is possible to find an [[interpretation (logic)|interpretation]] ([[model theory|model]]) that makes the formula true.<ref>See, for example, Boolos and Jeffrey, 1974, chapter 11.</ref>  A formula is ''valid'' if all interpretations make the formula true. The opposites of these concepts are unsatisfiability and invalidity, that is, a formula is ''unsatisfiable'' if none of the interpretations make the formula true, and ''invalid'' if some such interpretation makes the formula false. These four concepts are related to each other in a manner exactly analogous to [[Aristotle]]'s [[square of opposition]].}}

{{term|[[search algorithm]]}}
{{defn|Any {{gli|algorithm}} which solves the [[search problem]], namely, to retrieve information stored within some data structure, or calculated in the [[Feasible region|search space]] of a [[domain (software engineering)|problem domain]], either with [[Continuous or discrete variable|discrete or continuous value]]s.}}

{{term|[[selection (genetic algorithm)|selection]]}}
{{defn|The stage of a {{gli|genetic algorithm}} in which individual genomes are chosen from a population for later breeding (using the [[Crossover (genetic algorithm)|crossover operator]]).}}

{{term|[[self-management (computer science)|self-management]]}}
{{defn|The process by which computer systems manage their own operation without human intervention.}}

{{term|[[semantic network]]}}
{{ghat|Also '''frame network'''.}}
{{defn|A [[knowledge base]] that represents [[semantic]] relations between [[concept]]s in a network. This is often used as a form of [[knowledge representation]]. It is a [[directed graph|directed]] or [[undirected graph]] consisting of [[vertice]]s, which represent [[concept]]s, and [[graph theory|edge]]s, which represent [[semantic relation]]s between concepts,<ref name="Sowa">{{Cite encyclopedia |year=1987 |title=Semantic Networks |encyclopedia=Encyclopedia of Artificial Intelligence |url=https://jfsowa.com/pubs/semnet.htm |access-date=2008-04-29 |author-link=John F. Sowa |editor-last=Shapiro |editor-first=Stuart C |last1=Sowa |first1=John F.}}</ref> mapping or connecting [[semantic field]]s.}}

{{term|[[semantic reasoner]]}}
{{ghat|Also '''reasoning engine''', '''rules engine''', or simply '''reasoner'''.}}
{{defn|A piece of software able to infer [[logical consequence]]s from a set of asserted facts or [[axiom]]s. The notion of a semantic reasoner generalizes that of an [[inference engine]], by providing a richer set of mechanisms to work with. The [[inference rule]]s are commonly specified by means of an [[ontology language]], and often a [[description logic]] language. Many reasoners use [[first-order predicate logic]] to perform reasoning; inference commonly proceeds by {{gli|forward chaining}} and {{gli|backward chaining}}.}}

{{term|[[semantic query]]}}
{{defn|Allows for queries and analytics of associative and [[Contextualization (computer science)|contextual]] nature. Semantic queries enable the retrieval of both explicitly and implicitly derived information based on syntactic, semantic and structural information contained in data. They are designed to deliver precise results (possibly the distinctive selection of one single piece of information) or to answer more fuzzy and wide-open questions through [[pattern matching]] and [[Reasoning system|digital reasoning]].}}

{{term|[[semantics (computer science)|semantic]]s}}
{{defn|In [[programming language theory]], semantics is the field concerned with the rigorous mathematical study of the meaning of {{gli|programming language|programming languages}}. It does so by evaluating the meaning of [[programming language syntax|syntactically]] valid [[String (computer science)|string]]s defined by a specific programming language, showing the computation involved. In such a case that the evaluation would be of syntactically invalid strings, the result would be non-computation. Semantics describes the processes a computer follows when executing a program in that specific language. This can be shown by describing the relationship between the input and output of a program, or an explanation of how the program will be executed on a certain [[computer platform|platform]], hence creating a [[model of computation]].}}

{{anchor|semi-supervised learning}}{{term|[[Weak supervision|semi-supervised learning]]}}
{{ghat|Also '''weak supervision'''.}}
{{defn|A {{gli|machine learning}} training paradigm characterized by using a combination of a small amount of human-labeled data (used exclusively in {{gli|supervised learning}}), followed by a large amount of unlabeled data (used exclusively in {{gli|unsupervised learning}}).}}

{{term|[[sensor fusion]]}}
{{defn|The combining of [[sensor]]y data or data derived from disparate sources such that the resulting [[information]] has less uncertainty than would be possible when these sources were used individually.}}

{{term|[[separation logic]]}}
{{defn|An extension of [[Hoare logic]], a way of reasoning about programs. The assertion language of separation logic is a special case of the [[logic of bunched implications]] (BI).<ref name="bi">{{Cite journal |last1=O'Hearn |first1=P. W. |last2=Pym |first2=D. J. |date=June 1999 |title=The Logic of Bunched Implications |journal=[[Bulletin of Symbolic Logic]] |volume=5 |pages=215–244 |citeseerx=10.1.1.27.4742 |doi=10.2307/421090 |jstor=421090 |number=2|s2cid=2948552 }}</ref>}}

{{term|[[similarity learning]]}}
{{defn|An area of {{gli|supervised learning}} closely related to {{gli|classification}} and {{gli|regression}}, but the goal is to learn from a similarity function that measures how similar or related two objects are. It has applications in [[ranking]], in [[recommendation system]]s, visual identity tracking, face verification, and speaker verification.}}

{{term|[[simulated annealing]] (SA)}}
{{defn|A [[probabilistic algorithm|probabilistic technique]] for approximating the [[global optimum]] of a given [[function (mathematics)|function]]. Specifically, it is a [[metaheuristic]] to approximate [[global optimization]] in a large [[solution space|search space]] for an [[optimization problem]].}}

{{term|[[Artificial intelligence, situated approach|situated approach]]}}
{{defn|In artificial intelligence research, the situated approach builds agents that are designed to behave effectively successfully in their environment. This requires designing AI "from the bottom-up" by focussing on the basic perceptual and motor skills required to survive. The situated approach gives a much lower priority to abstract reasoning or problem-solving skills.}}

{{term|[[situation calculus]]}}
{{defn|A [[logic]] formalism designed for representing and reasoning about dynamical domains.}}

{{term|[[SLD resolution|Selective Linear Definite clause resolution]]}}
{{ghat|Also simply '''SLD resolution'''.}}
{{defn|The basic [[inference rule]] used in [[logic programming]]. It is a refinement of [[Resolution (logic)|resolution]], which is both [[Soundness|sound]] and refutation [[Completeness (logic)|complete]] for [[Horn clause]]s.}}

{{term|[[software]]}}
{{defn|A collection of [[data (computing)|data]] or [[computer]] instructions that tell the computer how to work. This is in contrast to [[Computer hardware|physical hardware]], from which the system is built and actually performs the work. In [[computer science]] and [[software engineering]], computer software is all [[information]] processed by [[computer system]]s, [[Computer program|program]]s and [[data]]. Computer software includes [[computer program]]s, [[Library (computing)|libraries]] and related non-executable [[Data (computing)|data]], such as [[Software documentation|online documentation]] or [[digital media]].}}

{{term|[[software engineering]]}}
{{defn|The application of [[engineering]] to the [[software development|development]] of [[software]] in a systematic method.<ref name="BoDu04">{{harvnb |Abran |Moore |Bourque| Dupuis |2004 |pp=1–1}}</ref><ref>{{Cite web |url=https://computingcareers.acm.org/?page_id=12 |title=Computing Degrees & Careers |year=2007 |publisher=ACM |url-status=dead |archive-url=https://web.archive.org/web/20110617053818/https://computingcareers.acm.org/?page_id=12 |archive-date=17 June 2011 |access-date=2010-11-23}}</ref><ref>{{Cite book |last1=Laplante |first1=Phillip |url=https://books.google.com/books?id=pFHYk0KWAEgC&q=What%20Every%20Engineer%20Should%20Know%20about%20Software%20Engineering.&pg=PA1 |title=What Every Engineer Should Know about Software Engineering |publisher=CRC |year=2007 |isbn=978-0-8493-7228-5 |location=Boca Raton |access-date=2011-01-21}}</ref>}}

{{term|[[spatial-temporal reasoning]]}}
{{defn|An area of artificial intelligence which draws from the fields of [[computer science]], [[cognitive science]], and [[cognitive psychology]]. The theoretic goal—on the cognitive side—involves representing and reasoning spatial-temporal knowledge in mind. The applied goal—on the computing side—involves developing high-level control systems of automata for [[robotic navigation|navigating]] and understanding time and space.}}

{{term|[[SPARQL]]}}
{{defn|An [[RDF query language]]—that is, a [[Semantic Query|semantic]] [[query language]] for [[database]]s—able to retrieve and manipulate data stored in [[Resource Description Framework|Resource Description Framework (RDF)]] format.<ref>{{Cite web |url=https://eweek.com/development/sparql-will-make-the-web-shine |title=SPARQL Will Make the Web Shine |last1=Rapoza |first1=Jim |date=2 May 2006 |website=[[eWeek]] |access-date=2007-01-17}}</ref><ref>{{Cite book |last1=Segaran |first1=Toby |title=Programming the Semantic Web |url=https://archive.org/details/programmingseman00sega_683 |url-access=limited |last2=Evans |first2=Colin |last3=Taylor |first3=Jamie |publisher=O'Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472 |year=2009 |isbn=978-0-596-15381-6 |page=[https://archive.org/details/programmingseman00sega_683/page/n101 84]}}</ref>}}

{{term|[[sparse dictionary learning]]}}
{{ghat|Also '''sparse coding''' or '''SDL'''.}}
{{defn|A {{gli|feature learning}} method aimed at finding a [[Sparse matrix|sparse]] representation of the input data in the form of a linear combination of basic elements as well as those basic elements themselves.}}

{{term|[[speech recognition]]}}
{{defn|An interdisciplinary subfield of [[computational linguistics]] that develops methodologies and technologies that enables the recognition and [[translation]] of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech to text (STT). It incorporates knowledge and research in the [[linguistics]], [[computer science]], and [[electrical engineering]] fields.}}

{{anchor|spiking neural network}}{{term|[[spiking neural network]] (SNN)}}
{{defn|An {{gli|artificial neural network}} that more closely mimics a natural neural network.<ref name="Maas 1996">{{Cite journal |last1=Maass |first1=Wolfgang |year=1997 |title=Networks of spiking neurons: The third generation of neural network models |journal=Neural Networks |volume=10 |issue=9 |pages=1659–1671 |doi=10.1016/S0893-6080(97)00011-7 |issn=0893-6080}}</ref> In addition to [[Artificial neuron|neuronal]] and [[Electrical synapse|synaptic]] state, SNNs incorporate the concept of time into their [[Operating Model]].}}

{{term|[[state (computer science)|state]]}}
{{defn|In [[information technology]] and [[computer science]], a program is described as stateful if it is designed to remember preceding events or user interactions;<ref>{{Cite web |url=https://whatis.techtarget.com/definition/stateless |title=What is stateless? - Definition from WhatIs.com |website=techtarget.com}}</ref> the remembered information is called the state of the system.}}

{{anchor|classification}}{{term|[[statistical classification]]}}
{{defn|In {{gli|machine learning}} and [[statistics]], classification is the problem of identifying to which of a set of [[categorical data|categories]] (sub-populations) a new observation belongs, on the basis of a [[training set]] of data containing observations (or instances) whose category membership is known. Examples are assigning a given email to the [[Spam filtering|"spam" or "non-spam"]] class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (sex, blood pressure, presence or absence of certain symptoms, etc.). Classification is an example of [[pattern recognition]].}}

{{anchor|SARSA}}{{term|[[state–action–reward–state–action]] (SARSA)}}
{{defn|A {{gli|reinforcement learning}} {{gli|algorithm}} for learning a {{gli|markov decision process|Markov decision process}} policy.}}

{{anchor|statistical relational learning}}{{term|[[statistical relational learning]] (SRL)}}
{{defn|A subdiscipline of artificial intelligence and {{gli|machine learning}} that is concerned with [[domain model]]s that exhibit both [[uncertainty]] (which can be dealt with using statistical methods) and complex, [[relation (mathematics)|relational]] structure.<ref name="Getoor2007">[[Lise Getoor]] and [[Ben Taskar]]: ''[https://books.google.com/books?id=lSkIewOw2WoC Introduction to statistical relational learning]'', MIT Press, 2007</ref><ref>Ryan A. Rossi, Luke K. McDowell, David W. Aha, and Jennifer Neville, "[https://jair.org/media/3659/live-3659-6589-jair.pdf  Transforming Graph Data for Statistical Relational Learning.] {{Webarchive|url=https://web.archive.org/web/20180106202217/http://www.jair.org/media/3659/live-3659-6589-jair.pdf |date=6 January 2018 }}"  ''Journal of Artificial Intelligence Research (JAIR)'', '''Volume 45''' (2012), pp. 363–441.</ref> Note that SRL is sometimes called Relational Machine Learning (RML) in the literature. Typically, the [[knowledge representation]] formalisms developed in SRL use (a subset of) [[first-order logic]] to describe relational properties of a domain in a general manner ([[universal quantification]]) and draw upon [[probabilistic graphical model]]s (such as [[Bayesian network]]s or [[Markov network]]s) to model the uncertainty; some also build upon the methods of [[inductive logic programming]].}}

{{term|[[stochastic optimization]] (SO)}}
{{defn|Any [[optimization]] [[iterative method|method]] that generates and uses [[random variable]]s. For stochastic problems, the random variables appear in the formulation of the optimization problem itself, which involves random [[objective function]]s or random constraints. Stochastic optimization methods also include methods with random iterates. Some stochastic optimization methods use random iterates to solve stochastic problems, combining both meanings of stochastic optimization.<ref name="spall2003">{{Cite book |last1=Spall |first1=J. C. |url=https://jhuapl.edu/ISSO |title=Introduction to Stochastic Search and Optimization |publisher=Wiley |year=2003 |isbn=978-0-471-33052-3}}</ref> Stochastic optimization methods generalize [[deterministic system (mathematics)|deterministic]] methods for deterministic problems.}}

{{term|[[stochastic semantic analysis]]}}
{{defn|An approach used in [[computer science]] as a [[semantic]] component of [[natural language understanding]]. Stochastic models generally use the definition of segments of words as basic semantic units for the semantic models, and in some cases involve a two layered approach.<ref>''Language Understanding Using Two-Level Stochastic Models'' by F. Pla, et al, 2001, Springer Lecture Notes in Computer Science {{ISBN|978-3-540-42557-1}}</ref>}}

{{anchor|Stanford Research Institute Problem Solver}}{{term|[[Stanford Research Institute Problem Solver]] (STRIPS)}}
{{defn|An [[automated planning|automated planner]] developed by [[Richard Fikes]] and [[Nils Nilsson (researcher)|Nils Nilsson]] in 1971 at [[SRI International]].}}

{{anchor|subject-matter expert}}{{term|[[subject-matter expert]] (SME)}}
{{defn|A person who has accumulated great knowledge in a particular field or topic, demonstrated by the person's degree, licensure, and/or through years of professional experience with the subject.}}

{{term|[[superintelligence]]}}
{{defn|A hypothetical {{gli|intelligent agent|agent}} that possesses [[intelligence]] far surpassing that of the [[genius|brightest]] and most [[gifted]] human minds. Superintelligence may also refer to a property of problem-solving systems (e.g., superintelligent language translators or engineering assistants) whether or not these high-level intellectual competencies are embodied in agents that act within the physical world. A superintelligence may or may not be created by an {{gli|intelligence explosion}} and be associated with a {{gli|technological singularity}}.}}

{{term|[[supervised learning]]}}
{{defn|The {{gli|machine learning}} task of learning a function that maps an input to an output based on example input-output pairs.<ref>Stuart J. Russell, Peter Norvig (2010) ''[[Artificial Intelligence: A Modern Approach]], Third Edition'', Prentice Hall {{ISBN|9780136042594}}.</ref> It infers a function from ''{{vanchor|labeled [[training set|training data]]|LABELLED_DATA}}'' consisting of a set of ''training examples''.<ref>[[Mehryar Mohri]], Afshin Rostamizadeh, Ameet Talwalkar (2012) ''Foundations of Machine Learning'', The MIT Press {{ISBN|9780262018258}}.</ref>  In supervised learning, each example is a ''pair'' consisting of an input object (typically a vector) and a desired output value (also called the ''supervisory signal''). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to {{gli|generalization|generalize}} from the training data to unseen situations in a "reasonable" way (see [[inductive bias]]).}}

{{term|[[support vector machine]]s}}
{{defn|In {{gli|machine learning}}, support vector machines (SVMs, also support vector networks<ref>{{Cite journal |last1=Cortes |first1=Corinna |last2=Vapnik |first2=Vladimir N |year=1995 |title=Support vector networks |journal=Machine Learning |volume=20 |issue=3 |pages=273–297 |doi=10.1007/BF00994018 |doi-access=free}}</ref>) are {{gli|supervised learning}} models with associated learning {{gli|algorithm|algorithms}} that analyze data used for {{gli|classification}} and {{gli|regression}}.}}

{{anchor|swarm intelligence}}{{term|[[swarm intelligence]] (SI)}}
{{defn|The [[collective behavior]] of [[decentralized]], [[self-organized]] systems, either natural or artificial. The expression was introduced in the context of cellular robotic systems.<ref>{{Cite book |last1=Beni|first1=G. |last2=Wang |first2=J. |title=Proceed. NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy, June 26–30 (1989) |year=1993 |isbn=978-3-642-63461-1 |pages=703–712 |chapter=Swarm Intelligence in Cellular Robotic Systems |doi=10.1007/978-3-642-58069-7_38}}</ref>}}

{{term|[[symbolic artificial intelligence]]}}
{{defn|The term for the collection of all methods in {{gli|artificial intelligence}} research that are based on high-level "symbolic" (human-readable) representations of problems, [[Formal logic|logic]], and {{gli|search algorithm|search}}.}}

{{anchor|synthetic intelligence}}{{term|[[synthetic intelligence]] (SI)}}
{{defn|An alternative term for {{gli|artificial intelligence}} which emphasizes that the intelligence of machines need not be an imitation or in any way artificial; it can be a genuine form of intelligence.{{sfn|Haugeland|1985|p=255}}{{sfn|Poole|Mackworth|Goebel|1998|p=1}}}}

{{term|[[systems neuroscience]]}}
{{defn|A subdiscipline of [[neuroscience]] and [[systems biology]] that studies the structure and function of neural circuits and systems. It is an umbrella term, encompassing a number of areas of study concerned with how [[nerve cell]]s behave when connected together to form [[neural pathway]]s, [[neural circuit]]s, and larger [[large scale brain networks|brain network]]s.}}
{{glossaryend}}

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==T==
{{glossary}}
{{term|[[technological singularity]]}}
{{ghat|Also simply '''the singularity'''.}}
{{defn|A [[hypothetical]] point in the future when technological growth becomes uncontrollable and irreversible, resulting in unfathomable changes to human civilization.<ref>{{Cite news |url=https://singularitysymposium.com/definition-of-singularity.html |title=Collection of sources defining "singularity" |website=singularitysymposium.com |access-date=17 April 2019}}</ref><ref name="Singularity hypotheses">{{Cite book |last1=Eden |first1=Amnon H. |title=Singularity hypotheses: A Scientific and Philosophical Assessment |url=https://archive.org/details/singularityhypot00mueh |url-access=limited |last2=Moor, James H. |date=2012 |publisher=Springer |isbn=9783642325601 |location=Dordrecht |pages=[https://archive.org/details/singularityhypot00mueh/page/n9 1]–2}}</ref><ref>Cadwalladr, Carole (2014). "[https://theguardian.com/technology/2014/feb/22/robots-google-ray-kurzweil-terminator-singularity-artificial-intelligence Are the robots about to rise? Google's new director of engineering thinks so...]" ''The Guardian''. Guardian News and Media Limited.</ref>}}

{{term|[[temporal difference learning]]}}
{{defn|A class of [[Model-free (reinforcement learning)|model-free]] {{gli|reinforcement learning}} methods which learn by [[Bootstrapping (statistics)|bootstrapping]] from the current estimate of the value function. These methods sample from the environment, like [[Monte Carlo method]]s, and perform updates based on current estimates, like [[dynamic programming]] methods.<ref name="RSutton-1998">{{Cite book |last1=Sutton |first1=Richard |url=https://incompleteideas.net/sutton/book/the-book.html |title=Reinforcement Learning |last2=Andrew Barto |publisher=MIT Press |year=1998 |isbn=978-0-585-02445-5 |archive-url=https://web.archive.org/web/20170330005640/https://incompleteideas.net/sutton/book/the-book.html |archive-date=2017-03-30 |url-status=dead |name-list-style=amp}}</ref>}}

{{term|[[tensor network theory]]}}
{{defn|A theory of [[brain]] function (particularly that of the [[cerebellum]]) that provides a mathematical model of the [[transformation geometry|transformation]] of sensory [[space-time]] coordinates into motor coordinates and vice versa by cerebellar [[neuronal network]]s. The theory was developed as a [[geometrization]] of brain function (especially of the [[central nervous system]]) using [[tensor]]s.<!--  --><ref name="Neuroscience1980-Pellionisz">{{Cite journal |last1=Pellionisz |first1=A. |last2=Llinás |first2=R. |year=1980 |title=Tensorial Approach To The Geometry Of Brain Function: Cerebellar Coordination Via A Metric Tensor |url=https://academia.edu/download/31409354/pellionisz_1980_cerebellar_coordination_via_a_metric_tensor_fullpaper.pdf |journal=Neuroscience |volume=5 |issue=7 |pages=1125––1136 |doi=10.1016/0306-4522(80)90191-8 |pmid=6967569 |s2cid=17303132 }}{{dead link|date=July 2022|bot=medic}}{{cbignore|bot=medic}}</ref><!--  --><ref name="Neuroscience1985-Pellionisz">{{Cite journal |last1=Pellionisz |first1=A. |last2=Llinás |first2=R. |year=1985 |title=Tensor Network Theory Of The Metaorganization Of Functional Geometries In The Central Nervous System |journal=Neuroscience |volume=16 |issue=2 |pages=245–273 |doi=10.1016/0306-4522(85)90001-6 |pmid=4080158|s2cid=10747593 }}</ref>}}

{{term|[[TensorFlow]]}}
{{defn|A [[Free software|free]] and {{gli|open-source software|open-source}} [[software library]] for [[dataflow programming|dataflow]] and [[differentiable programming|differentiable]] programming across a range of tasks. It is a symbolic math library, and is also used for {{gli|machine learning}} applications such as {{gli|neural network|neural networks}}.<ref name="YoutubeClip">[https://youtube.com/watch?v=oZikw5k_2FM "TensorFlow: Open source machine learning"] "It is machine learning software being used for various kinds of perceptual and language understanding tasks" — Jeffrey Dean, minute 0:47 / 2:17 from YouTube clip</ref>}}

{{term|[[theoretical computer science]] (TCS)}}
{{defn|A subset of general [[computer science]] and [[mathematics]] that focuses on more mathematical topics of computing and includes the [[theory of computation]].}}

{{term|[[theory of computation]]}}
{{defn|In [[theoretical computer science]] and [[mathematics]], the theory of computation is the branch that deals with how efficiently problems can be solved on a [[model of computation]], using an {{gli|algorithm}}. The field is divided into three major branches: [[automata theory]] and languages, [[computability theory]], and {{gli|computational complexity theory}}, which are linked by the question: ''"What are the fundamental capabilities and limitations of computers?".''<ref name="Sipser-3rd">{{Cite book |last1=Sipser |first1=Michael |title=Introduction to the Theory of Computation 3rd |publisher=Cengage Learning |year=2013 |isbn=978-1-133-18779-0 |quote=central areas of the theory of computation: automata, computability, and complexity. (Page 1) |author-link=Michael Sipser}}</ref>}}

{{term|[[Thompson sampling]]}}
{{defn|A {{gli|heuristic}} for choosing actions that addresses the exploration-exploitation dilemma in the [[multi-armed bandit]] problem. It consists in choosing the action that maximizes the expected reward with respect to a randomly drawn belief.<ref>{{Cite journal |last1=Thompson |first1=William R |year=1933 |title=On the likelihood that one unknown probability exceeds another in view of the evidence of two samples |journal=Biometrika |volume=25 |issue=3–4 |pages=285–294 |doi=10.1093/biomet/25.3-4.285}}</ref><ref>{{Cite journal |last1=Russo |first1=Daniel J. |last2=Van Roy |first2=Benjamin |last3=Kazerouni |first3=Abbas |last4=Osband |first4=Ian |last5=Wen |first5=Zheng |year=2018 |title=A Tutorial on Thompson Sampling |journal=Foundations and Trends in Machine Learning |volume=11 |issue=1 |pages=1–96 |arxiv=1707.02038 |doi=10.1561/2200000070|s2cid=3929917 }}</ref>}}

{{term|[[time complexity]]}}
{{defn|The [[computational complexity]] that describes the amount of time it takes to run an {{gli|algorithm}}. Time complexity is commonly estimated by counting the number of elementary operations performed by the algorithm, supposing that each elementary operation takes a fixed amount of time to perform. Thus, the amount of time taken and the number of elementary operations performed by the algorithm are taken to differ by at most a [[constant factor]].}}

{{term|[[transfer learning]]}}
{{defn|A {{gli|machine learning}} technique in which knowledge learned from a task is reused in order to boost performance on a related task.<ref>{{cite web |last1=West |first1=Jeremy |first2=Dan |last2=Ventura |first3=Sean |last3=Warnick |url=http://cpms.byu.edu/springresearch/abstract-entry?id=861 |title=Spring Research Presentation: A Theoretical Foundation for Inductive Transfer |publisher=Brigham Young University, College of Physical and Mathematical Sciences |year=2007 |access-date=2007-08-05 |url-status=dead |archive-url=https://web.archive.org/web/20070801120743/http://cpms.byu.edu/springresearch/abstract-entry?id=861 |archive-date=2007-08-01 }}</ref> For example, for [[Computer vision|image classification]], knowledge gained while learning to [[Computer vision#Recognition|recognize]] cars could be applied when trying to recognize trucks.}}

{{anchor|transformer}}{{term|[[Transformer (deep learning architecture)|transformer]]}}
{{defn|A type of {{gli|deep learning}} architecture that exploits a multi-head {{gli|attention mechanism}}. Transformers address some of the limitations of {{gli|long short-term memory}}, and became widely used in {{gli|natural language processing}}, although it can also process other types of data such as images in the case of [[vision transformer]]s.<ref>{{Cite web |last=Dickson |first=Ben |title=Machine learning: What is the transformer architecture? |url=https://bdtechtalks.com/2022/05/02/what-is-the-transformer/ |access-date=2 May 2022 |website=TechTarget |date=2 May 2022 |language=en}}</ref>}}

{{term|[[transhumanism]]}}
{{ghat|Abbreviated '''H+''' or '''h+'''.}}
{{defn|An international [[school of thought|philosophical movement]] that advocates for the transformation of the [[human condition]] by developing and making widely available sophisticated technologies to greatly [[human enhancement|enhance human]] intellect and physiology.<ref name="Mercer">{{Cite book |last1=Mercer |first1=Calvin |title=Religion and Transhumanism: The Unknown Future of Human Enhancement |publisher=Praeger}}</ref><ref name="Bostrom 2005">{{Cite journal |last1=Bostrom |first1=Nick |author-link=Nick Bostrom |year=2005 |title=A history of transhumanist thought |url=https://nickbostrom.com/papers/history.pdf |journal=[[Journal of Evolution and Technology]] |access-date=February 21, 2006}}</ref>}}

{{term|[[transition system]]}}
{{defn|In [[theoretical computer science]], a transition system is a concept used in the study of [[computation]]. It is used to describe the potential behavior of {{gli|discrete system|discrete systems}}. It consists of [[state (computer science)|state]]s and transitions between states, which may be labeled with labels chosen from a set; the same label may appear on more than one transition. If the label set is a [[Singleton (mathematics)|singleton]], the system is essentially unlabeled, and a simpler definition that omits the labels is possible.}}

{{term|[[tree traversal]]}}
{{ghat|Also '''tree search'''.}}
{{defn|A form of [[graph traversal]] and refers to the process of visiting (checking and/or updating) each node in a [[tree data structure]], exactly once. Such traversals are classified by the order in which the nodes are visited.}}

{{term|[[true quantified Boolean formula]]}}
{{defn|In {{gli|computational complexity theory}}, the language TQBF is a {{gli|formal language}} consisting of the true quantified Boolean formulas. A (fully) quantified Boolean formula is a formula in [[Quantification (logic)|quantified]] [[propositional logic]] where every variable is quantified (or [[Bound variable|bound]]), using either [[existential quantification|existential]] or [[universal quantification|universal]] quantifiers, at the beginning of the sentence. Such a formula is equivalent to either true or false (since there are no [[Bound variable|free]] variables). If such a formula evaluates to true, then that formula is in the language TQBF. It is also known as QSAT (Quantified {{gli|Boolean satisfiability problem|SAT}}).}}

{{term|[[Turing machine]]}}
{{defn|A [[mathematical model of computation]] describing an [[abstract machine]]<ref>Minsky 1967:107 "In his 1936 paper, A. M. Turing defined the class of abstract machines that now bear his name. A Turing machine is a finite-state machine associated with a special kind of environment – its tape – in which it can store (and later recover) sequences of symbols," also Stone 1972:8 where the word "machine" is in quotation marks.</ref> that manipulates symbols on a strip of tape according to a table of rules.<ref>Stone 1972:8 states "This "machine" is an abstract mathematical model", also cf. Sipser 2006:137ff that describes the "Turing machine model". Rogers 1987 (1967):13 refers to "Turing's characterization", Boolos Burgess and Jeffrey 2002:25 refers to a "specific kind of idealized machine".</ref> Despite the model's simplicity, it is capable of implementing any {{gli|algorithm}}.<ref>Sipser 2006:137 "A Turing machine can do everything that a real computer can do".</ref>}}

{{term|[[Turing test]]}}
{{defn|A test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human, developed by [[Alan Turing]] in 1950. Turing proposed that a human evaluator would [[natural language understanding|judge natural language conversation]]s between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation is a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel such as a [[computer keyboard]] and [[visual display unit|screen]] so the result would not depend on the machine's ability to render words as speech.<ref>Turing originally suggested a [[teleprinter]], one of the few text-only communication systems available in 1950. {{Harv|Turing|1950|p=433}}</ref> If the evaluator cannot reliably tell the machine from the human, the machine is said to have passed the test. The test results do not depend on the machine's ability to give correct answers to questions, only how closely its answers resemble those a human would give.}}

{{term|[[type system]]}}
{{defn|In {{gli|programming language|programming languages}}, a set of rules that assigns a property called [[type (computer science)|type]] to the various constructs of a [[computer program]], such as [[variable (computer science)|variable]]s, [[expression (computer science)|expression]]s, [[function (computer science)|function]]s, or [[modular programming|module]]s.{{sfn|Pierce|2002|p=1|ps=: "A type system is a tractable syntactic method for proving the absence of certain program behaviors by classifying phrases according to the kinds of values they compute."}} These types formalize and enforce the otherwise implicit categories the programmer uses for [[algebraic data type]]s, data structures, or other components (e.g. "string", "array of float", "function returning boolean"). The main purpose of a type system is to reduce possibilities for [[bug (computer programming)|bug]]s in computer programs{{sfn|Cardelli|2004|p=1|ps=: "The fundamental purpose of a type system is to prevent the occurrence of execution errors during the running of a program."}} by defining [[Interface (computer science)|interface]]s between different parts of a computer program, and then checking that the parts have been connected in a consistent way. This checking can happen statically (at [[compile time]]), dynamically (at [[run time (program lifecycle phase)|run time]]), or as a combination of static and dynamic checking. Type systems have other purposes as well, such as expressing business rules, enabling certain compiler optimizations, allowing for [[multiple dispatch]], providing a form of documentation, etc.}}
{{glossaryend}}

{{Compact TOC|side=yes|center=yes|top=yes|num=yes|extlinks=yes|seealso=yes|refs=yes|nobreak=yes|}}

==U==
{{glossary}}
{{term|[[unsupervised learning]]}}
{{defn|A type of self-organized [[Hebbian learning]] that helps find previously unknown patterns in data set without pre-existing labels. It is also known as [[self-organization]] and allows modeling [[probability densities]] of given inputs.<ref name="Hinton99a">{{Cite book |last1=Hinton |first1=Jeffrey |title=Unsupervised Learning: Foundations of Neural Computation |last2=Sejnowski |first2=Terrence |publisher=MIT Press |year=1999 |isbn=978-0262581684}}</ref> It is one of the three basic paradigms of {{gli|machine learning}}, alongside {{gli|supervised learning|supervised}} and {{gli|reinforcement learning}}. {{gli|semi-supervised learning|Semi-supervised learning}} has also been described and is a hybridization of supervised and unsupervised techniques.}}
{{glossaryend}}

==V==
{{glossary}}
{{term|[[vision processing unit]] (VPU)}}
{{defn|A type of [[microprocessor]] designed to [[hardware acceleration|accelerate]] {{gli|machine vision}} tasks.<ref>{{Cite web |url=https://tomshardware.com/news/movidiud-myriad2-vpu-vision-processing-vr,30850.html |title=A third type of processor for AR/VR: Movidius' Myriad 2 VPU |last1=Colaner |first1=Seth |last2=Humrick |first2=Matthew |date=January 3, 2016 |website=Tom's Hardware}}</ref><ref>{{Cite web |url=https://digit.in/general/the-rise-of-vpus-giving-eyes-to-machines-29561.html |title=The rise of VPUs: Giving Eyes to Machines |last1=Banerje |first1=Prasid |date=March 28, 2016 |website=Digit.in |access-date=5 July 2022 |archive-date=11 December 2018 |archive-url=https://web.archive.org/web/20181211221429/https://www.digit.in/general/the-rise-of-vpus-giving-eyes-to-machines-29561.html |url-status=dead }}</ref>}}
{{term|Value-alignment complete}}
{{defn|{{citation needed span|Analogous to an [[AI-complete]] problem, a value-alignment complete problem is a problem where the [[AI control problem]] needs to be fully solved to solve it.|date=January 2019}}}}
{{glossaryend}}

==W==
{{glossary}}
{{term|[[Watson (computer)|Watson]]}}
{{defn|A [[question answering|question-answering]] computer system capable of answering questions posed in [[natural language]],<ref name="ibm">{{Cite web |url=https://research.ibm.com/deepqa/faq.shtml |title=DeepQA Project: FAQ |website=IBM |access-date=February 11, 2011 |archive-date=5 November 2015 |archive-url=https://web.archive.org/web/20151105125722/https://www.research.ibm.com/deepqa/faq.shtml |url-status=dead }}</ref> developed in [[IBM]]'s DeepQA project by a research team led by [[principal investigator]] [[David Ferrucci]].<ref>{{Cite journal |last1=Ferrucci |first1=David |last2=Levas |first2=Anthony |last3=Bagchi |first3=Sugato |last4=Gondek |first4=David |last5=Mueller |first5=Erik T. |date=2013-06-01 |title=Watson: Beyond Jeopardy! |journal=Artificial Intelligence |volume=199 |pages=93–105 |doi=10.1016/j.artint.2012.06.009 |doi-access=free}}</ref> Watson was named after IBM's first CEO, industrialist [[Thomas J. Watson]].<ref name="NYT_20110208">{{Cite news |last1=Hale |first1=Mike |url=https://nytimes.com/2011/02/09/arts/television/09nova.html |title=Actors and Their Roles for $300, HAL? HAL! |date=February 8, 2011 |work=[[The New York Times]] |access-date=February 11, 2011}}</ref><ref>{{Cite web |url=https://research.ibm.com/deepqa/deepqa.shtml |title=The DeepQA Project |website=IBM Research |access-date=February 18, 2011 |archive-date=21 January 2013 |archive-url=https://web.archive.org/web/20130121103239/http://www.research.ibm.com/deepqa/deepqa.shtml |url-status=dead }}</ref>}}

{{term|[[weak AI]]}}
{{ghat|Also '''narrow AI'''.}}
{{defn|{{gli|artificial intelligence|Artificial intelligence}} that is focused on one narrow task.<ref>io9.com mentions narrow AI. Published 1 April 2013. Retrieved 16 February 2014: https://io9.com/how-much-longer-before-our-first-ai-catastrophe-464043243</ref><ref>AI researcher Ben Goertzel explains why he became interested in AGI instead of narrow AI. Published 18 Oct 2013. Retrieved 16 February 2014. https://intelligence.org/2013/10/18/ben-goertzel/</ref><ref>TechCrunch discusses AI App building regarding Narrow AI. Published 16 Oct 2015. Retrieved 17 Oct 2015. https://techcrunch.com/2015/10/15/machine-learning-its-the-hard-problems-that-are-valuable/</ref>}}

{{term|[[weak supervision]]}}
{{defn|See {{gli|semi-supervised learning}}.}}

{{term|[[word embedding]]}}
{{defn|A representation of a word in {{gli|natural language processing}}. Typically, the representation is a [[real number|real-valued]] vector that encodes the meaning of the word in such a way that words that are closer in the vector space are expected to be similar in meaning.<ref>{{cite book |last1=Jurafsky |first1=Daniel |last2=H. James |first2=Martin |title=Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition |date=2000 |publisher=Prentice Hall |location=Upper Saddle River, N.J. |isbn=978-0-13-095069-7 |url=https://web.stanford.edu/~jurafsky/slp3/}}</ref>}}
{{glossaryend}}

==X==
{{glossary}}
{{term|[[XGBoost]]}}
{{defn|Short for eXtreme Gradient Boosting, XGBoost<ref name="source-code">{{cite web |url=https://github.com/dmlc/xgboost |title=GitHub project webpage |website=[[GitHub]] |date=June 2022 |access-date=2016-04-05 |archive-date=2021-04-01 |archive-url=https://web.archive.org/web/20210401110045/https://github.com/dmlc/xgboost |url-status=live }}</ref> is an [[Open-source software|open-source]] [[Library (computing)|software library]] which provides a {{gli|regularization|regularizing}} {{gli|gradient boosting}} framework for multiple programming languages.}}
{{glossaryend}}

{{Compact TOC|side=yes|center=yes|top=yes|num=yes|extlinks=yes|seealso=yes|refs=yes|nobreak=yes|}}

==References==
{{reflist}}

===Works cited===
{{refbegin|2}}
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* {{cite book |first1=Alain |last1=Abran |first2=James W. |last2=Moore |first3=Pierre |last3=Bourque |first4=Robert |last4=Dupuis |first5=Leonard L. |last5=Tripp |title=Guide to the Software Engineering Body of Knowledge |year=2004 |publisher=IEEE |isbn=978-0-7695-2330-9}}
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* {{cite book |first=Luca |last=Cardelli |author-link=Luca Cardelli |editor=Allen B. Tucker |title=CRC Handbook of Computer Science and Engineering |edition=2nd |chapter=Type systems |year=2004 |publisher=CRC Press |chapter-url=http://lucacardelli.name/Papers/TypeSystems.pdf |isbn=978-1584883609}}
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* {{cite book |last=Haugeland |first=John |author-link=John Haugeland |year=1985 |title=Artificial Intelligence: The Very Idea |publisher=MIT Press |location=Cambridge, Mass. |isbn=978-0-262-08153-5}}
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* {{cite arXiv |last1=Legg |first1=Shane |last2=Hutter |first2=Marcus |date=15 June 2007 |title=A Collection of Definitions of Intelligence |class=cs.AI |eprint=0706.3639}}
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* {{Cite book |last=Mitchell |first=Melanie |title=An Introduction to Genetic Algorithms |year=1996 |publisher=MIT Press |location=Cambridge, MA |isbn=9780585030944}}
<!-- N -->
* {{cite book |last=Nilsson |first=Nils |author-link=Nils Nilsson (researcher) |year=1998 |title=Artificial Intelligence: A New Synthesis |url=https://archive.org/details/artificialintell0000nils |url-access=registration |publisher=Morgan Kaufmann |isbn=978-1-55860-467-4 |access-date=18 November 2019 |archive-date=26 July 2020 |archive-url=https://web.archive.org/web/20200726131654/https://archive.org/details/artificialintell0000nils |url-status=live}}
<!-- P -->
* {{cite book |first=Benjamin C. |last=Pierce |author-link=Benjamin C. Pierce |year=2002 |title=Types and Programming Languages |publisher=MIT Press |isbn=978-0-262-16209-8}}
* {{cite book |first1=David |last1=Poole |author-link=David Poole (researcher) |first2=Alan |last2=Mackworth |author2-link=Alan Mackworth |first3=Randy |last3=Goebel |author3-link=Randy Goebel |year=1998 |title=Computational Intelligence: A Logical Approach |publisher=Oxford University Press |location=New York |isbn=978-0-19-510270-3 |url=https://archive.org/details/computationalint00pool |access-date=22 August 2020 |archive-date=26 July 2020 |archive-url=https://web.archive.org/web/20200726131436/https://archive.org/details/computationalint00pool |url-status=live}}
<!-- R -->
* {{Russell Norvig 2003}}
<!-- T -->
* {{Turing 1950}}
{{refend}}

==Notes==
{{reflist|group=Note}}

{{Differentiable computing}}
{{Software engineering}}
{{Computer science}}
{{Evolutionary computation}}
{{emerging technologies|topics=yes|infocom=yes}}
{{Robotics}}
{{Glossaries of computers}}
{{Glossaries of science and engineering}}

[[Category:Glossaries of science|Artificial intelligence]]
[[Category:Machine learning]]
[[Category:Glossaries of technology|Artificial intelligence]]
[[Category:Wikipedia glossaries using description lists]]