Probabilistic Inference by Projected Weighted Model Counting on Horn Clauses
Keywords: Projected Weighted Model Counting, Bayesian Networks, Graph Connectivity Estimation
TL;DR: This paper has been published at CP2023 and presents a new way of encoding some well-known probabilistic inference problems (based on projected weighted model counting and Horn formulas) and a new solver for such encoding.
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.
Submission Number: 6
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