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Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.CP.2023.15
URN: urn:nbn:de:0030-drops-190520
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/19052/
Dubray, Alexandre ;
Schaus, Pierre ;
Nijssen, Siegfried
Probabilistic Inference by Projected Weighted Model Counting on Horn Clauses
Abstract
Weighted model counting, that is, counting the weighted number of satisfying assignments of a propositional formula, is an important tool in probabilistic reasoning. Recently, the use of projected weighted model counting (PWMC) has been proposed as an approach to formulate and answer probabilistic queries. In this work, we propose a new simplified modeling language based on PWMC in which probabilistic inference tasks are modeled using a conjunction of Horn clauses and a particular weighting scheme for the variables. We show that the major problems of inference for Bayesian Networks, network reachability and probabilistic logic programming can be modeled in this language. Subsequently, we propose a new, relatively simple solver that is specifically optimized to solve the PWMC problem for such formulas. Our experiments show that our new solver is competitive with state-of-the-art solvers on the major problems studied.
BibTeX - Entry
@InProceedings{dubray_et_al:LIPIcs.CP.2023.15,
author = {Dubray, Alexandre and Schaus, Pierre and Nijssen, Siegfried},
title = {{Probabilistic Inference by Projected Weighted Model Counting on Horn Clauses}},
booktitle = {29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
pages = {15:1--15:17},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-300-3},
ISSN = {1868-8969},
year = {2023},
volume = {280},
editor = {Yap, Roland H. C.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2023/19052},
URN = {urn:nbn:de:0030-drops-190520},
doi = {10.4230/LIPIcs.CP.2023.15},
annote = {Keywords: Model Counting, Bayesian Networks, Probabilistic Networks}
}
