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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Renkens, J., Van den Broeck, G., Nijssen, S. (2012). k-Optimal: A Novel Approximate Inference Algorithm for ProbLog. In: Muggleton, S.H., Tamaddoni-Nezhad, A., Lisi, F.A. (eds) Inductive Logic Programming. ILP 2011. Lecture Notes in Computer Science(), vol 7207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31951-8_7
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DOI: https://doi.org/10.1007/978-3-642-31951-8_7
Publisher Name: Springer, Berlin, Heidelberg
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