Transcript Document

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
2008. 04.18
Summarized by Dongjoo Lee, IDS Lab., Seoul National University
Presented by Dongjoo Lee, IDS Lab., Seoul National University
Contents

EasyMeeting

Vigil

Services

Architecture

Context Broker Architecture (Cobra)

COBRA-ONT

Context Reasoning

Privacy Protection

Conclusion
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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


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Provide relevant services and information to meeting participants on the basis of their contexts.
Context Broker
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Provide a centralized model of context that all devices, services, and agents in the space can share
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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
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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.
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EasyMeeting 
Specialized server entities that facilitate system communication, clientrole management, and service-access control.


Clients, services, and Vigil managers
Role-based inference mechanism to control access to services


Vigil
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 

Speech understanding

CCML (Centaurus Capability Markup Language)

IBM WebSphere Voice Server SDK, Voice XML
Presentation

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AppleScript commands
Lighting control
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
Services
X10 technology
Music

MP3 music player software

Greeting

Profile display

Web-based server application
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EasyMeeting -
Architecture
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)
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Context Broker Architecture (Cobra)

Jena reasoning API – OWL ontologies

Jess rule-based engine – domain specific reasoning
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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
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 Example
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Context Reasoning

Jena rule engine – ontology axioms

Java Expert System Shell (JESS) – forward-chaining inference
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Algorithm

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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 -
Assumption-based reasoning
Harry is in Room RM338
Harry intends to give a presentation in meetting m1203
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Privacy 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 -
Example
RDF Notation 3 Syntax
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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.
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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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