{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T13:26:55Z","timestamp":1770470815678,"version":"3.49.0"},"reference-count":40,"publisher":"Cambridge University Press (CUP)","issue":"3","license":[{"start":{"date-parts":[[2014,4,15]],"date-time":"2014-04-15T00:00:00Z","timestamp":1397520000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Theory and Practice of Logic Programming"],"published-print":{"date-parts":[[2015,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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.<\/jats:p>","DOI":"10.1017\/s1471068414000076","type":"journal-article","created":{"date-parts":[[2014,4,15]],"date-time":"2014-04-15T13:25:21Z","timestamp":1397568321000},"page":"358-401","source":"Crossref","is-referenced-by-count":155,"title":["Inference and learning in probabilistic logic programs using weighted Boolean formulas"],"prefix":"10.1017","volume":"15","author":[{"given":"DAAN","family":"FIERENS","sequence":"first","affiliation":[]},{"given":"GUY","family":"VAN DEN BROECK","sequence":"additional","affiliation":[]},{"given":"JORIS","family":"RENKENS","sequence":"additional","affiliation":[]},{"given":"DIMITAR","family":"SHTERIONOV","sequence":"additional","affiliation":[]},{"given":"BERND","family":"GUTMANN","sequence":"additional","affiliation":[]},{"given":"INGO","family":"THON","sequence":"additional","affiliation":[]},{"given":"GERDA","family":"JANSSENS","sequence":"additional","affiliation":[]},{"given":"LUC","family":"DE RAEDT","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2014,4,15]]},"reference":[{"key":"S1471068414000076_ref15","unstructured":"Gomes C. 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S. and Rocha R. 2010. On the implementation of the probabilistic logic programming language problog. In Theory and Practice of Logic Programming Systems, 24th International Conference on Logic Programming (ICLP 2008), Special Issue, vol. 11, pp. 235\u2013262, arXiv: CoRR abs\/1006.4442."},{"key":"S1471068414000076_ref5","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511811357"},{"key":"S1471068414000076_ref21","unstructured":"Ishihata M. , Kameya Y. , Sato T. and Minato S. 2008. Propositionalizing the EM algorithm by BDDs. In Late Breaking Papers of the 18th International Conference on Inductive Logic Programming."},{"key":"S1471068414000076_ref37","doi-asserted-by":"crossref","unstructured":"Sato T. and Kameya Y. 2008. Probabilistic Inductive Logic Programming \u2013 Theory and Applications, Chapter: \u201cNew Advances in Logic-Based Probabilistic Modeling by PRISM.\u201d Lecture Notes in Computer Science. 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