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Approximate Reasoning in MAS: Rough Set Approach
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Web Intelligence archiveProceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence table of contents
Pages 12-18
Year of Publication: 2006
ISBN:0-7695-2747-7
Author
Andrzej Skowron
Warsaw University, Poland
Publisher
IEEE Computer Society Washington, DC, USA
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10.1109/WI.2006.43
ABSTRACT
In modeling multiagent systems for real-life problems, techniques for approximate reasoning about vague concepts and dependencies (ARVCD) are necessary. We discuss an approach to approximate reasoning based on rough sets. In particular, we present a number of basic concepts such as approximation spaces, concept approximation, rough inclusion, construction of information granules in calculi of information granules, and perception logic. The approach to ARVCD is illustrated by examples relative to interactions of agents, ontology approximation, adaptive hierarchical learning of compound concepts and skills, behavioral pattern identification, planning, conflict analysis and negotiations, and perception-based reasoning.
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INDEX TERMS
Primary Classification: I. Computing Methodologies I.2 ARTIFICIAL INTELLIGENCE I.2.4 Knowledge Representation Formalisms and Methods
Additional Classification: I. Computing Methodologies I.2 ARTIFICIAL INTELLIGENCE I.2.11 Distributed Artificial Intelligence Subjects: Multiagent systems I.2.3 Deduction and Theorem Proving