Learning the Parameters of Probabilistic Logic Programs from Interpretations

  • Bernd Gutmann
  • Ingo Thon
  • Luc De Raedt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6911)

Abstract

ProbLog is a recently introduced probabilistic extension of the logic programming language Prolog, in which facts can be annotated with the probability that they hold. The advantage of this probabilistic language is that it naturally expresses a generative process over interpretations using a declarative model. Interpretations are relational descriptions or possible worlds. This paper introduces a novel parameter estimation algorithm LFI-ProbLog for learning ProbLog programs from partial interpretations. The algorithm is essentially a Soft-EM algorithm. It constructs a propositional logic formula for each interpretation that is used to estimate the marginals of the probabilistic parameters. The LFI-ProbLog algorithm has been experimentally evaluated on a number of data sets that justifies the approach and shows its effectiveness.

Keywords

Logic Program Kullback Leibler Boolean Formula Binary Decision Diagram Ground Atom 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Bernd Gutmann
    • 1
  • Ingo Thon
    • 1
  • Luc De Raedt
    • 1
  1. 1.Department of Computer ScienceKatholieke Universiteit LeuvenHeverleeBelgium

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