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Intelligent Agents Intelligent Agents Meet the Semantic Web Meet the Semantic Web in Smart Spaces 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 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

Intelligent Agents Meet the Semantic Web in Smart Spaces

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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 - PowerPoint PPT Presentation

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Page 1: 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

Page 2: Intelligent Agents Meet the Semantic Web in Smart Spaces

Copyright 2008 by CEBT

ContentsContents

EasyMeeting

Vigil

Services

Architecture

Context Broker Architecture (Cobra)

COBRA-ONT

Context Reasoning

Privacy Protection

Conclusion

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Page 3: Intelligent Agents Meet the Semantic Web in Smart Spaces

Copyright 2008 by CEBT

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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Page 4: Intelligent Agents Meet the Semantic Web in Smart Spaces

Copyright 2008 by CEBT

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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Page 5: Intelligent Agents Meet the Semantic Web in Smart Spaces

Copyright 2008 by CEBT

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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Page 6: Intelligent Agents Meet the Semantic Web in Smart Spaces

Copyright 2008 by CEBT

EasyMeeting - EasyMeeting - ArchitectureArchitecture

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Page 7: Intelligent Agents Meet the Semantic Web in Smart Spaces

Copyright 2008 by CEBT

Context Broker Architecture (Cobra)Context Broker Architecture (Cobra)

Jena reasoning API – OWL ontologies

Jess rule-based engine – domain specific reasoning

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Page 8: Intelligent Agents Meet the Semantic Web in Smart Spaces

Copyright 2008 by CEBT

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

Page 9: Intelligent Agents Meet the Semantic Web in Smart Spaces

Copyright 2008 by CEBT

User Profile ExampleUser Profile Example

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Page 10: Intelligent Agents Meet the Semantic Web in Smart Spaces

Copyright 2008 by CEBT

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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Page 11: Intelligent Agents Meet the Semantic Web in Smart Spaces

Copyright 2008 by CEBT

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

Page 12: Intelligent Agents Meet the Semantic Web in Smart Spaces

Copyright 2008 by CEBT

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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Page 13: Intelligent Agents Meet the Semantic Web in Smart Spaces

Copyright 2008 by CEBT

Privacy Protection - Privacy Protection - ExampleExample

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Page 14: Intelligent Agents Meet the Semantic Web in Smart Spaces

Copyright 2008 by CEBT

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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Page 15: Intelligent Agents Meet the Semantic Web in Smart Spaces

Copyright 2008 by CEBT

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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