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LPMEME: A statistical method for inductive logic programming

  • Learning I: Induction
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Advances in Artifical Intelligence (Canadian AI 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1081))

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Abstract

This paper describes LPMEME, a new learning algorithm for inductive logic programming that uses statistical techniques to find first-order patterns. LPMEME takes as input examples in the form of logical facts and outputs a first-order theory that is represented to some degree in all of the examples. LPMEME uses an underlying statistical model whose parameters are learned using expectation maximization, an iterative gradient descent method for maximum likelihood parameter estimation. The underlying statistical model is described and the EM algorithm developed. Experimental tests show that LPMEME can learn first-order concepts and can be used to find approximate solutions to the subgraph isomorphism problem.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering 0114, University of California, San Diego, 92093, La Jolla, CA

    Karan Bhatia & Charles Elkan

Authors
  1. Karan Bhatia
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  2. Charles Elkan
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Editor information

Gordon McCalla

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© 1996 Springer-Verlag Berlin Heidelberg

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Bhatia, K., Elkan, C. (1996). LPMEME: A statistical method for inductive logic programming. In: McCalla, G. (eds) Advances in Artifical Intelligence. Canadian AI 1996. Lecture Notes in Computer Science, vol 1081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61291-2_54

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  • DOI: https://doi.org/10.1007/3-540-61291-2_54

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61291-9

  • Online ISBN: 978-3-540-68450-3

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