Intelligent Agents Meet the Semantic Web in Smart Spaces
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Transcript of Intelligent Agents Meet the Semantic Web in Smart Spaces
Intelligent AgentsIntelligent AgentsMeet the Semantic WebMeet the Semantic Webin Smart Spacesin 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. 04.18Summarized by Dongjoo Lee, IDS Lab., Seoul National University
Presented by Dongjoo Lee, IDS Lab., Seoul National University
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ContentsContents
EasyMeeting
Vigil
Services
Architecture
Context Broker Architecture (Cobra)
COBRA-ONT
Context Reasoning
Privacy Protection
Conclusion
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EasyMeetingEasyMeeting
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.
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EasyMeeting - EasyMeeting - VigilVigil
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.
Deontic concept
– Rights, prohibitions, obligations, and dispensations
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EasyMeeting - EasyMeeting - ServicesServices
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
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EasyMeeting - EasyMeeting - ArchitectureArchitecture
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Context Broker Architecture (Cobra)Context Broker Architecture (Cobra)
Jena reasoning API – OWL ontologies
Jess rule-based engine – domain specific reasoning
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COBRA-ONTCOBRA-ONT
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
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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
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User Profile ExampleUser Profile Example
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Context ReasoningContext Reasoning
Jena rule engine – ontolog axioms
Java Expert System Shell (JESS) – forwared-chaining inference
Algorithm
Ontology inference
1) Jess rule execution
2) select the type of context it attempt to infer
3) decide whether it can infer this type of context using only ontology reasoning
Logic inference
4) Find all essential supporting facts by querying the ontology model
5) Convert RDF representation into the Jess representation
6) Executing the predefined forward-chaining procedure
7) Add newly deduced facts to ontology model
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Context Reasoning - Context Reasoning - Assumption-based Assumption-based reasoningreasoning
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Harry is in Room RM338
Harry intends to give a presentation in meetting m1203
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Privacy ProtectionPrivacy Protection
Users can define customized policy rules to permit or forbid access to their private information in various granularity.
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Privacy Protection - Privacy Protection - ExampleExample
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Feedback from DemonstrationsFeedback 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.
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ConclusionConclusion
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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