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Parameterized Logic Programs where Computing Meets Learning

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Functional and Logic Programming (FLOPS 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2024))

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Abstract

In this paper, we describe recent attempts to incorporate learning into logic programs as a step toward adaptive software that can learn from an environment. Although there are a variety of types of learn- ing, we focus on parameter learning of logic programs, one for statistical learning by the EM algorithm and the other for reinforcement learning by learning automatons. Both attempts are not full- edged yet, but in the former case, thanks to the general framework and an effcient EM learning algorithm combined with a tabulated search, we have obtained very promising results that open up the prospect of modeling complex symbolic-statistical phenomena.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science, Tokyo Institute of Technology, 2-12-1 Ôokayama Meguro-ku, Tokyo, Japan, 152

    Taisuke Sato

Authors
  1. Taisuke Sato
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Editor information

Editors and Affiliations

  1. Institut für Wirtschaftsinformatik, WestfälischeWilhelms-Universität Münster, Steinfurter Straße 109, 48149, Münster, Germany

    Herbert Kuchen

  2. Department of Information and Computer Science, Waseda University, 4-1, Okubo 3-chome, Shinjuku-ku, Tokyo, 169-8555, Japan

    Kazunori Ueda

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© 2001 Springer-Verlag Berlin Heidelberg

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Sato, T. (2001). Parameterized Logic Programs where Computing Meets Learning. In: Kuchen, H., Ueda, K. (eds) Functional and Logic Programming. FLOPS 2001. Lecture Notes in Computer Science, vol 2024. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44716-4_3

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  • DOI: https://doi.org/10.1007/3-540-44716-4_3

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41739-2

  • Online ISBN: 978-3-540-44716-0

  • eBook Packages: Springer Book Archive

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