Learning Structure and Parameters of Stochastic Logic Programs

  • Stephen Muggleton
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2583)

Abstract

Previous papers have studied learning of Stochastic Logic Programs (SLPs) either as a purely parametric estimation problem or separated structure learning and parameter estimation into separate phases. In this paper we consider ways in which both the structure and the parameters of an SLP can be learned simultaneously. The paper assumes an ILP algorithm, such as Progol or FOIL, in which clauses are constructed independently. We derive analytical and numerical methods for efficient computation of the optimal probability parameters for a single clause choice within such a search.

Keywords

Stochastic logic programs generalisation analytical methods numerical methods 

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References

  1. 1.
    J. Cussens. Loglinear models for first-order probabilistic reasoning. In Proceedings of the 15th Annual Conference on Uncertainty in Artificial Intelligence, pages 126–133, San Francisco, 1999. Kaufmann.Google Scholar
  2. 2.
    J. Cussens. Parameter estimation in stochastic logic programs. Machine Learning, 2000. In press.Google Scholar
  3. 3.
    A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, Series B, 39:1–38, 1977.MATHMathSciNetGoogle Scholar
  4. 4.
    N. Friedman, L. Getoor, D. Koller, and A. Pfeffer. Learning probabilistic relational models. In IJCAI-99: Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pages 1300–1309, San Mateo, CA:, 1999. Morgan-Kaufmann.Google Scholar
  5. 5.
    D. Koller and A. Pfeffer. Learning probabilities for noisy first-order rules. In IJCAI-97: Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, pages 1316–1321, San Mateo, CA:, 1997. Morgan-Kaufmann.Google Scholar
  6. 6.
    S. Muggleton. Inverse entailment and Progol. New Generation Computing, 13:245–286, 1995.CrossRefGoogle Scholar
  7. 7.
    S. Muggleton. Inductive logic programming: issues, results and the LLL challenge. Artificial Intelligence, 114(1–2):283–296, December 1999.MATHCrossRefGoogle Scholar
  8. 8.
    S. Muggleton. Learning stochastic logic programs. Electronic Transactions in Artificial Intelligence, 5(041), 2000.Google Scholar
  9. 9.
    S. Muggleton. Learning from positive data. Machine Learning, 2001. Accepted subject to revision.Google Scholar
  10. 10.
    S. Muggleton and C. Feng. Efficient induction of logic programs. In Proceedings of the First Conference on Algorithmic Learning Theory, pages 368–381, Tokyo, 1990. Ohmsha.Google Scholar
  11. 11.
    S. H. Muggleton. Stochastic logic programs. In L. de Raedt, editor, Advances in Inductive Logic Programming, pages 254–264. IOS Press, 1996.Google Scholar
  12. 12.
    S. H. Muggleton. Learning stochastic logic programs. In Lise Getoor and David Jensen, editors, Proceedings of the AAAI2000 workshop on Learning Statistical Models from Relational Data. AAAI, 2000.Google Scholar
  13. 13.
    G. Plotkin. A further note on inductive generalization. In Machine Intelligence, volume 6. Edinburgh University Press, 1971.Google Scholar
  14. 14.
    J. R. Quinlan. Learning logical definitions from relations. Machine Learning, 5:239–266, 1990.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Stephen Muggleton
    • 1
  1. 1.Department of ComputingImperial CollegeLondonUK

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