TY - JOUR

T1 - Inference and learning in probabilistic logic programs using weighted Boolean formulas

AU - Fierens, Daan

AU - Van Den Broeck, Guy

AU - Renkens, Joris

AU - Shterionov, Dimitar

AU - Gutmann, Bernd

AU - Thon, Ingo

AU - Janssens, Gerda

AU - De Raedt, Luc

PY - 2015

Y1 - 2015

N2 - Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for probabilistic logic programs. Several such tasks, such as computing the marginals, given evidence and learning from (partial) interpretations, have not really been addressed for probabilistic logic programs before. The first contribution of this paper is a suite of efficient algorithms for various inference tasks. It is based on the conversion of the program and the queries and evidence to a weighted Boolean formula. This allows us to reduce inference tasks to well-studied tasks, such as weighted model counting, which can be solved using state-of-the-art methods known from the graphical model and knowledge compilation literature. The second contribution is an algorithm for parameter estimation in the learning from interpretations setting. The algorithm employs expectation-maximization, and is built on top of the developed inference algorithms. The proposed approach is experimentally evaluated. The results show that the inference algorithms improve upon the state of the art in probabilistic logic programming, and that it is indeed possible to learn the parameters of a probabilistic logic program from interpretations.

AB - Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for probabilistic logic programs. Several such tasks, such as computing the marginals, given evidence and learning from (partial) interpretations, have not really been addressed for probabilistic logic programs before. The first contribution of this paper is a suite of efficient algorithms for various inference tasks. It is based on the conversion of the program and the queries and evidence to a weighted Boolean formula. This allows us to reduce inference tasks to well-studied tasks, such as weighted model counting, which can be solved using state-of-the-art methods known from the graphical model and knowledge compilation literature. The second contribution is an algorithm for parameter estimation in the learning from interpretations setting. The algorithm employs expectation-maximization, and is built on top of the developed inference algorithms. The proposed approach is experimentally evaluated. The results show that the inference algorithms improve upon the state of the art in probabilistic logic programming, and that it is indeed possible to learn the parameters of a probabilistic logic program from interpretations.

KW - parameter learning

KW - probabilistic inference

KW - probabilistic logic programming

UR - http://www.scopus.com/inward/record.url?scp=84925800236&partnerID=8YFLogxK

U2 - 10.1017/S1471068414000076

DO - 10.1017/S1471068414000076

M3 - Article

AN - SCOPUS:84925800236

SN - 1471-0684

VL - 15

SP - 358

EP - 401

JO - Theory and Practice of Logic Programming

JF - Theory and Practice of Logic Programming

IS - 3

ER -