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 developed at the Institute for Artificial Intelligence at the University of Bremen, Germany.
The system
KnowRob can serve as a common sense 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 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]
Goals
The goal of the KnowRob framework is to make semantic knowledge available for service robots.
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.