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k-Optimal: A Novel Approximate Inference Algorithm for ProbLog

  • Joris Renkens
  • Guy Van den Broeck
  • Siegfried Nijssen
Conference paper
  • 779 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7207)

Abstract

ProbLog is a probabilistic extension of Prolog. Given the complexity of exact inference under ProbLog’s semantics, in many applications in machine learning approximate inference is necessary. Current approximate inference algorithms for ProbLog however require either dealing with large numbers of proofs or do not guarantee a low approximation error. In this paper we introduce a new approximate inference algorithm which addresses these shortcomings. Given a user-specified parameter k, this algorithm approximates the success probability of a query based on at most k proofs and ensures that the calculated probability p is (1 − 1/e)p * ≤ p ≤ p *, where p * is the highest probability that can be calculated based on any set of k proofs.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Joris Renkens
    • 1
  • Guy Van den Broeck
    • 1
  • Siegfried Nijssen
    • 1
  1. 1.Department of Computer ScienceKatholieke Universiteit LeuvenHeverleeBelgium

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