Vidal's libraryTitle: | 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. |
Cited by 31 - Google Scholar
@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