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Semantic Knowledge Model and Architecture for Agents in Discrete Environments

2. Outline. MotivationState of the ArtObjectivesMethodology and ToolsAgent Knowledge Model ? Models, Methodology, LibraryApplicationsConclusion. 3. Motivation and State of the Art. MAS is powerful paradigm for distributed or heterogeneous systemsMAS need Knowledge Support and SemanticsMAS ne

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Semantic Knowledge Model and Architecture for Agents in Discrete Environments

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    1. Semantic Knowledge Model and Architecture for Agents in Discrete Environments Michal Laclavík Ústav informatiky Slovenskej akadémie vied Dear Charman, Commission members, reviewers, guestsDear Charman, Commission members, reviewers, guests

    2. 2 Outline Motivation State of the Art Objectives Methodology and Tools Agent Knowledge Model – Models, Methodology, Library Applications Conclusion

    3. 3 Motivation and State of the Art MAS is powerful paradigm for distributed or heterogeneous systems MAS need Knowledge Support and Semantics MAS need Connection with Existing Commercial Standards Agent Technology Roadmap: Current MAS Systems – lack of Internal Agent Knowledge Model, lack of interconnection with semantic web results (knowledge model representations) and commercial standards Focus on Agents and Knowledge representation (Ontologies) Knowledge Management and Experience Management as application domains

    4. 4 State of the Art - Agents Agent Definition: An agent is a computer system capable of flexible autonomous action in a dynamic, unpredictable and open environment. (LUCK 2003) MAS Standards: FIPA, MASIF Related to agent communication, agent platforms No standards for internal agent knowledge model with available implementations

    5. 5 State of the Art - Agents Architectures: Reactive Architecture No specification of knowledge model, behavior of agent is based on implemented responses to environment states Belief Desire Intention Architecture – BDI Belief – represents knowledge model, available some implementations based on logic programming, not used in FIPA compliant MAS Behavioral Architecture FIPA compliant MAS are based on such architecture No specification of Internal Agent Knowledge model – depend on agent designer and developer JADE Agent System Support for ontologies based on FIPA-SL (Similar to First Order Logic) No Query engine No Storage No Inference

    6. 6 State of the Art – Ontologies, Knowledge Ontologies Knowledge Representation OWL-DL compatible with Description Logic Query and Storage Engines available RDF, OWL, RDQL based Application domain Knowledge Management (KM) is the process through which organizations generate value from their intellectual and knowledge-based assets (Source: CIO Magazine) Experience Management is special kind of KM – based on “lessons learned”

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