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Intelligent Agents Meet the Semantic Web in Smart Spaces

Intelligent Agents Meet the Semantic Web in Smart Spaces. Harry Chen,Tim Finin, Anupam Joshi, and Lalana Kagal University of Maryland, Baltimore County Filip Perich Cougaar Software Dipanjan Chakraborty IBM India Research Laboratory

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Intelligent Agents Meet the Semantic Web in Smart Spaces

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  1. Intelligent AgentsMeet the Semantic Webin Smart Spaces Harry Chen,Tim Finin, Anupam Joshi, and Lalana Kagal University of Maryland, Baltimore County Filip Perich Cougaar Software Dipanjan Chakraborty IBM India Research Laboratory IEEE INTERNET COMPUTING, NOVEMBER, OCTOBER 2004, Published by the IEEE Computer Society 2008. 04.18 Summarized by Dongjoo Lee, IDS Lab., Seoul National University Presented by Dongjoo Lee, IDS Lab., Seoul National University

  2. Contents • EasyMeeting • Vigil • Services • Architecture • Context Broker Architecture (Cobra) • COBRA-ONT • Context Reasoning • Privacy Protection • Conclusion

  3. EasyMeeting • A pervasive computing system that supports users in a smart meeting-room environment in which a distributed system of intelligent agents, services, devices, and sensors share a common goal; • Goal • Provide relevant services and information to meeting participants on the basis of their contexts. • Differences • Uses OWL for expressing ontologies to • support context modeling and knowledge sharing • detect and resolve inconsistent context knowledge • protect the user’s privacy.

  4. EasyMeeting - Vigil • Specialized server entities that facilitate system communication, client-role management, and service-access control. • Clients, services, and Vigil managers • Role-based inference mechanism to control access to services • Role-permission definition • Reasoning of the role-assignment manager is built on the Rei framework. • Deonticconcept • Rights, prohibitions, obligations, and dispensations

  5. EasyMeeting - Services • Speech understanding • CCML (Centaurus Capability Markup Language) • IBM WebSphere Voice Server SDK, Voice XML • Presentation • AppleScript commands • Lighting control • X10 technology • Music • MP3 music player software • Greeting • Profile display • Web-based server application

  6. EasyMeeting - Architecture

  7. Context Broker Architecture (Cobra) Jena reasoning API – OWL ontologies Jess rule-based engine – domain specific reasoning

  8. COBRA-ONT • Integrated from other ontologies • FOAF • DAML-Time & the Entry Sub-ontology of Time • OpenCyc Spatial Ontologies & RCC • COBRA-ONT & MoGATU BDI Ontology • Rei Policy Ontology • Why OWL ? • Expressive knowledge-representation language • Have a normative syntax in RDF and XML • Has many predefined classes and properties • COBRA-ONT imports from SOUPA • Time, space, policy, social networks, actions, location context, documents, and events

  9. User Profile Example

  10. Context Reasoning • Jena rule engine – ontolog axioms • Java Expert System Shell (JESS) – forwared-chaining inference • Algorithm • Ontology inference • Jess rule execution • select the type of context it attempt to infer • decide whether it can infer this type of context using only ontology reasoning • Logic inference • Find all essential supporting facts by querying the ontology model • Convert RDF representation into the Jess representation • Executing the predefined forward-chaining procedure • Add newly deduced facts to ontology model

  11. Context Reasoning - Assumption-based reasoning Harry is in Room RM338 Harry intends to give a presentation in meetting m1203

  12. Privacy Protection Users can define customized policy rules to permit or forbid access to their private information in various granularity.

  13. Privacy Protection - Example

  14. Feedback from Demonstrations • From three external groups • UMBC university administrators, visitors from commercial companies and other universities • Critics • The system has a limited ability to handle unexpected situational changes • The workflow process was too rigid and could be unsuitable for everyday usage • Using policy to control how private information is shared doesn’t address other kinds of privacy concerns such as the logging and persistent storage of a user’s private information by the agents, and the possibility for the agents acquiring certain private user information by reasoning over an aggregated collection of their public information.

  15. Conclusion • The EasyMeeting and Cobra prototypes demonstrate the feasibility of using OWL ontologies to let distributed agents • share knowledge • reason about contextual information • express policies for user privacy protection • Challenging issues • Scalability of knowledge sharing in a distributed and dynamic environment • Performance and time complexity of context reasoning of a vast amount of sensing data • User-interface issues associated with editing and maintaining user privacy policies

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