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 IEEE INTERNET COMPUTING, NOVEMBER, OCTOBER 2004, Published by the IEEE Computer Society Summarized by Dongjoo Lee, IDS Lab., Seoul National University Presented by Dongjoo Lee, IDS Lab., Seoul National University

Copyright  2008 by CEBT Contents  EasyMeeting Vigil Services Architecture  Context Broker Architecture (Cobra)  COBRA-ONT  Context Reasoning  Privacy Protection  Conclusion 2

Copyright  2008 by CEBT 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.  Context Broker Provide a centralized model of context that all devices, services, and agents in the space can share Acquire contextual information from sources that are unreachable by the resource-limited devices Reason about contextual information that can’t be directly acquired from the sensors Detect and resolve inconsistent knowledge sotred in the shared context model Protect privacy by enforcing policies that users have defined to control the sharing and use of their contextual information  Differences Uses OWL for expressing ontologies to – support context modeling and knowledge sharing – detect and resolve inconsistent context knowledge – protect the user’s privacy. 3

Copyright  2008 by CEBT 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. Deontic concept – Rights, prohibitions, obligations, and dispensations 4

Copyright  2008 by CEBT 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 5

Copyright  2008 by CEBT EasyMeeting - Architecture 6 Presentation Schedule On 8 September 2004, 1:00 to 2:30 P.M. Room 338 device profile In Standard Device Ontology Harry (speaker) Hrabowski (the distinguished audience)

Copyright  2008 by CEBT Context Broker Architecture (Cobra)  Jena reasoning API – OWL ontologies  Jess rule-based engine – domain specific reasoning 7

Copyright  2008 by CEBT COBRA-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 8 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

Copyright  2008 by CEBT User Profile Example 9

Copyright  2008 by CEBT Context Reasoning  Jena rule engine – ontology axioms  Java Expert System Shell (JESS) – forward-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 10

Copyright  2008 by CEBT Context Reasoning - Assumption-based reasoning 11 Harry is in Room RM338 Harry intends to give a presentation in meetting m1203

Copyright  2008 by CEBT Privacy Protection  Users can define customized policy rules to permit or forbid access to their private information in various granularity. 12

Copyright  2008 by CEBT Privacy Protection - Example 13 RDF Notation 3 Syntax

Copyright  2008 by CEBT 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. 14

Copyright  2008 by CEBT 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 15