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Date
2012
Abstract
Cognitive robotics are autonomous systems capable of artificial reasoning. Such systems can be achieved with a logical approach, but still AI struggles to connect the abstract logic with real-world meanings. Knowledge representation and reasoning help to resolve this problem and to establish the vital connection between knowledge, perception, and action of a robot. Cognitive robots must use their knowledge against the perception of their world and generate appropriate actions in that world in compliance with some goals and beliefs. This paper presents an approach to multi-tier knowledge representation for cognitive robots, where ontologies are integrated with rules and Bayesian networks. The approach allows for efficient and comprehensive knowledge structuring and awareness based on logical and statistical reasoning.
Supervisor
Description
peer-reviewed
Publisher
IEEE Computer Society
Citation
Proceedings of the 15th IEEE International Symposium on Object/Component/Service-oriented Real-time Distributed Computing Workshops (ISCORCW 2012);
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Funding Information
European Research Council (ERC), Science Foundation Ireland (SFI)