MAP Inference for Probabilistic Logic Programming. Issue 5 (September 2020)

• Record Type: Journal Article
• Authors: BELLODI, ELENA; ALBERTI, MARCO; RIGUZZI, FABRIZIO; ZESE, RICCARDO
• Is Part Of: Theory and practice of logic programming. Volume 20:Issue 5(2020)
• Page Start: 641
• Page End: 655
• Publication Date: 2020-09
• Subjects: Logic programming -- Periodicals; Artificial intelligence -- Computer programs -- Periodicals; Constraint programming (Computer science) -- Periodicals; 005.115
• DOI: 10.1017/S1471068420000174
• Languages: English
• ISSNs: 1471-0684

Abstract

Abstract: In Probabilistic Logic Programming (PLP) the most commonly studied inference task is to compute the marginal probability of a query given a program. In this paper, we consider two other important tasks in the PLP setting: the Maximum-A-Posteriori (MAP) inference task, which determines the most likely values for a subset of the random variables given evidence on other variables, and the Most Probable Explanation (MPE) task, the instance of MAP where the query variables are the complement of the evidence variables. We present a novel algorithm, included in the PITA reasoner, which tackles these tasks by representing each problem as a Binary Decision Diagram and applying a dynamic programming procedure on it. We compare our algorithm with the version of ProbLog that admits annotated disjunctions and can perform MAP and MPE inference. Experiments on several synthetic datasets show that PITA outperforms ProbLog in many cases.