Agent-Based Knowledge Discovery

W. H. E. Davies, Peter Edwards

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

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Abstract

Agent-Based Knowledge Discovery provides a new technique for performing data-mining over distributed databases. By combining techniques from Distributed AI and Machine Learning, software agents equipped with learning algorithms mine local databases. These agents then co-operate to integrate the knowledge obtained, before presenting the results to the user. We are currently exploring the use of a new software agent language, Agent-K and the application of first order learning techniques to data-mining. However, the main area of investigation is how the agents should interact, and how the knowledge should be integrated.
Original languageEnglish
Title of host publicationProceedings of AAAI 1995 Spring Symposium on Information Gathering from Heterogeneous, Distributed Environments
EditorsCraig Knoblock, Alon Levy
PublisherAAAI Press
Pages34-37
VolumeSS-95-08
ISBN (Print)978-0-929280-91-2
Publication statusPublished - 1995

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Keywords

  • machine learning
  • intelligent agent
  • distributed learning

Cite this

Davies, W. H. E., & Edwards, P. (1995). Agent-Based Knowledge Discovery. In C. Knoblock, & A. Levy (Eds.), Proceedings of AAAI 1995 Spring Symposium on Information Gathering from Heterogeneous, Distributed Environments (Vol. SS-95-08, pp. 34-37). AAAI Press. https://www.aaai.org/Papers/Symposia/Spring/1995/SS-95-08/SS95-08-007.pdf