Experience-based resource description and selection in multiagent information retrieval

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper, we propose an agent-centric approach to resource description and selection in a multiagent information retrieval (IR). In the multiagent system, each agent learns from its experience through its interactions with other agents their capabilities and qualifications. Based on a distributed ontology learning framework, our methodology allows an agent to profile other agents in a dynamic translation table and a neighborhood profile, which together help determine resource description and selection process. Further, we report on the experiments and results of the first phase of our research, which focuses on the operational issues (e.g., real-time constraints, frequency of queries, number of threads, narrowness in ontology) on how the agents handle queries collaboratively.

Original languageEnglish (US)
Title of host publicationProceedings of the Seventeenth International FloridaArtificial Intelligence Research Society Conference, FLAIRS 2004
EditorsV. Barr, Z. Markov
Pages8-13
Number of pages6
StatePublished - 2004
EventProceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2004 - Miami Beach, FL, United States
Duration: May 17 2004May 19 2004

Publication series

NameProceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2004
Volume1

Conference

ConferenceProceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2004
CountryUnited States
CityMiami Beach, FL
Period5/17/045/19/04

ASJC Scopus subject areas

  • Engineering(all)

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