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Neural fuzzy agents for database search

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Cooperative Information Agents (CIA 1997)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1202))

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Abstract

A neural fuzzy system can help learn an agent profile of a user. The fuzzy system uses if-then rules to store and compress the agent's knowledge of the user's likes and dislikes. A neural system uses training data to form and tune the rules. The profile is a preference map or a bumpy utility surface over the space of search objects. Rules define fuzzy patches that cover the bumps as learning unfolds and as the fuzzy agent system gives a finer approximation of the profile. The agent system searches for preferred objects with the learned profile and a new fuzzy measure of similarity. We derive a new supervised learning law that tunes this matching measure with new sample data. Then we test the fuzzy agent profile system on object spaces of flowers and sunsets and test the fuzzy agent matching system on an object space of sunset images.

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Authors and Affiliations

  1. Signal and Image Processing Institute Department of Electrical Engineering-Systems, University of Southern California, 90089-2564, Los Angeles, California

    Sanya Mitaim & Bart Kosko

Authors
  1. Sanya Mitaim
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  2. Bart Kosko
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Editor information

Peter Kandzia Matthias Klusch

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© 1997 Springer-Verlag Berlin Heidelberg

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Mitaim, S., Kosko, B. (1997). Neural fuzzy agents for database search. In: Kandzia, P., Klusch, M. (eds) Cooperative Information Agents. CIA 1997. Lecture Notes in Computer Science, vol 1202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62591-7_31

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  • DOI: https://doi.org/10.1007/3-540-62591-7_31

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62591-9

  • Online ISBN: 978-3-540-68321-6

  • eBook Packages: Springer Book Archive

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