Vidal's library
Title: Learning to Share Meaning in a Multi-Agent System
Author: Andrew B. Williams
Journal: Autonomous Agents and Multi-Agent Systems
Volume: 8
Number: 2
Pages: 165--193
Year: 2004
DOI: 10.1023/B:AGNT.0000011160.45980.4b
Abstract: The development of the semantic Web will require agents to use common domain ontologies to facilitate communication of conceptual knowledge. However, the proliferation of domain ontologies may also result in conflicts between the meanings assigned to the various terms. That is, agents with diverse ontologies may use different terms to refer to the same meaning or the same term to refer to different meanings. Agents will need a method for learning and translating similar semantic concepts between diverse ontologies. Only until recently have researchers diverged from the last decade's "common ontology" paradigm to a paradigm involving agents that can share knowledge using diverse ontologies. This paper describes how we address this agent knowledge sharing problem of how agents deal with diverse ontologies by introducing a methodology and algorithms for multi-agent knowledge sharing and learning in a peer-to-peer setting. We demonstrate how this approach will enable multi-agent systems to assist groups of people in locating, translating, and sharing knowledge using our Distributed Ontology Gathering Group Integration Environment (DOGGIE) and describe our proof-of-concept experiments. DOGGIE synthesizes agent communication, machine learning, and reasoning for information sharing in the Web domain.

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@Article{williams04a,
  author =	 {Andrew B. Williams},
  title =	 {Learning to Share Meaning in a Multi-Agent System},
  journal =	 {Autonomous Agents and Multi-Agent Systems},
  year =	 2004,
  volume =	 8,
  number =	 2,
  pages =	 {165--193},
  abstract =	 {The development of the semantic Web will require
                  agents to use common domain ontologies to facilitate
                  communication of conceptual knowledge. However, the
                  proliferation of domain ontologies may also result
                  in conflicts between the meanings assigned to the
                  various terms. That is, agents with diverse
                  ontologies may use different terms to refer to the
                  same meaning or the same term to refer to different
                  meanings. Agents will need a method for learning and
                  translating similar semantic concepts between
                  diverse ontologies. Only until recently have
                  researchers diverged from the last decade's "common
                  ontology" paradigm to a paradigm involving agents
                  that can share knowledge using diverse
                  ontologies. This paper describes how we address this
                  agent knowledge sharing problem of how agents deal
                  with diverse ontologies by introducing a methodology
                  and algorithms for multi-agent knowledge sharing and
                  learning in a peer-to-peer setting. We demonstrate
                  how this approach will enable multi-agent systems to
                  assist groups of people in locating, translating,
                  and sharing knowledge using our Distributed Ontology
                  Gathering Group Integration Environment (DOGGIE) and
                  describe our proof-of-concept experiments. DOGGIE
                  synthesizes agent communication, machine learning,
                  and reasoning for information sharing in the Web
                  domain.},
  keywords =     {multiagent learning ontologies},
  url =		 {http://jmvidal.cse.sc.edu/library/williams04a.pdf},
  doi =		 {10.1023/B:AGNT.0000011160.45980.4b},
  googleid = 	 {WoBF4coMo6QJ:scholar.google.com/},
  cluster = 	 {11863339908926373978}
}
Last modified: Wed Mar 9 10:16:11 EST 2011