k-Optimal: A Novel Approximate Inference Algorithm for ProbLog

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


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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  1. 1.
    Van den Broeck, G., Thon, I., van Otterlo, M., De Raedt, L.: DTProbLog: A Decision-Theoretic Probabilistic Prolog. AAAI (2010)Google Scholar
  2. 2.
    Bryant, R.E.: Graph-Based Algorithms for Boolean Function Manipulation. IEEE Transactions on Computers 35, 677–691 (1986)zbMATHCrossRefGoogle Scholar
  3. 3.
    Cornuejols, G., Fisher, M.L., Nemhauser, G.L.: Location of bank accounts to optimize float: an analytic study of exact and approximate algorithms. Management Science (1977)Google Scholar
  4. 4.
    Hazan, E., Safra, S., Schwartz, O.: On the complexity of approximating k-set packing. Computational Complexity 15, 20–39 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Ourfali, O., Shlomi, T., Ideker, T., Ruppin, E., Sharan, R.: Spine: a framework for signaling-regulatory pathway inference from cause-effect experiments. Bioinformatics 23(13), 359–366 (2007)CrossRefGoogle Scholar
  6. 6.
    De Raedt, L., Kimmig, A., Toivonen, H.: Problog: A probabilistic prolog and its application in link discovery. In: IJCAI, pp. 2462–2467 (2007)Google Scholar
  7. 7.
    De Raedt, L., Kimmig, A., Gutmann, B., Kersting, K., Santos Costa, V., Toivonen, H.: Probabilistic inductive querying using ProbLog. In: Inductive Databases and Constraint-Based Data Mining, pp. 229–262 (2010)Google Scholar

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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