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 -