k-Optimal: a novel approximate inference algorithm for ProbLog
ProbLog is a probabilistic extension of Prolog. Given the complexity of exact inference under ProbLogs 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. Furthermore a useful feature of the set of calculated proofs is that it is diverse. Our experiments show the utility of the proposed algorithm.
approximative inference inductive logic programming statistical relational learning decision theory