Knowledge processing for robots
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KnowRob (Knowledge processing for robots) is a system which combines Knowledge representation and reasoning methods to acquire and ground knowledge. It can serve as a commonsense framework for the integration of knowledge. This knowledge can be static encyclopedic knowledge, common-sense knowledge, task descriptions, environment models, object information, observed actions, etc., which can come from different sources, like manually axiomatized, derived from observations, or imported from the web [1],[2]
To represent the knowledge, KnowRob uses the [OWL ontology language] and an extended [first-order logic] knowledge representation with computable predicates. To give the order of subactions, KnowRob includes a pair-wise ordering constrain, which gives a partial ordering. The knowledge is computed by external methods using Prolog queries.
KnowRob adopts the closed-world assumption from Prolog, and a open-world assumption by the use of computables[3]. To include reasoning rules into Prolog, KnowRob uses an inference procedure beyond the capabilities of OWL to extract information about tasks executions[4].
References
- ^ M. Tenorth and M. Beetz (2017) Representations for robot knowledge in the knowrob framework. Artificial Intelligence: Supplement C 247: 151-169. ISSN 0004-3702. doi:10.1016/j.artint.2015.05.010. Special Issue on AI and Robotics.
- ^ M. Tenorth, D. Jain, and M. Beetz (2010) Knowledge processing for cognitive robots. KI - Künstliche Intelligenz 24(3):233-240. ISSN 1610-1987. doi:10.1007/s13218-010-0044-0.
- ^ M. Tenorth and M. Beetz (2013) Knowrob: A knowledge processing infrastructure for cognition-enabled robots. The International Journal of Robotics Research 32(5):566-590. doi:10.1177/0278364913481635.
- ^ M. Tenorth, G. Bartels, and M. Beetz (2014) Knowledge-based specification of robot motions. Twenty-first European Conference on Artificial Intelligence (ECAI):873-878. Amsterdam, The Netherlands. ISBN 978-1-61499-418-3. doi:10.3233/978-1-61499-419-0-873.