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Publication

Knowledge representation with KnowLang: the marXbot Case Study

Date
2012
Abstract
Intelligent systems are capable of AI exhibited via knowledge representation and reasoning, which helps to connect abstract knowledge symbols to real-world meanings. This paper presents a formal language for knowledge representation called KnowLang. The language implies a multi-tier specification model emphasizing knowledge corpuses, knowledge base operators and inference primitives. The approach allows for efficient and comprehensive knowledge structuring where ontologies are integrated with rules and Bayesian networks. The paper presents the KnowLang specification constructs formally along with a case study based on a mobile robotics platform.
Supervisor
Description
peer-reviewed
Publisher
IEEE Computer Society
Citation
Proceedings of the 11th IEEE International Conference on Cybernetic Intelligent Systems;
Collections
Lero - The Science Foundation Ireland Research Centre for Software
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Vassev_2012_knowledge.pdf
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Keywords
knowledge representation, reasoning robotics
ULRR Identifiers
https://researchrepository.ul.ie/handle/10344/2730
 https://doi.org/10.34961/421
Funding code
Funding Information
European Research Council (ERC), Science Foundation Ireland (SFI)
Sustainable Development Goals
External Link
Type
Meetings and Proceedings
Rights
https://creativecommons.org/licenses/by-nc-sa/1.0/
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