Learning Branching Heuristics for Propositional Model Counting

• URL: http://arxiv.org/abs/2007.03204v2
• Date: Thu, 8 Sep 2022 21:47:20 GMT
• Title: Learning Branching Heuristics for Propositional Model Counting
• Authors: Pashootan Vaezipoor, Gil Lederman, Yuhuai Wu, Chris J. Maddison, Roger Grosse, Sanjit A. Seshia, Fahiem Bacchus
• Abstract summary: #SAT is the problem of computing the number of satisfying assignments of a Boolean formula. Neuro# is an approach for learning branchings to improve the performance of exact #SAT solvers on instances from a given family of problems.
• Score: 27.343084935521752
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Propositional model counting, or #SAT, is the problem of computing the number of satisfying assignments of a Boolean formula. Many problems from different application areas, including many discrete probabilistic inference problems, can be translated into model counting problems to be solved by #SAT solvers. Exact #SAT solvers, however, are often not scalable to industrial size instances. In this paper, we present Neuro#, an approach for learning branching heuristics to improve the performance of exact #SAT solvers on instances from a given family of problems. We experimentally show that our method reduces the step count on similarly distributed held-out instances and generalizes to much larger instances from the same problem family. It is able to achieve these results on a number of different problem families having very different structures. In addition to step count improvements, Neuro# can also achieve orders of magnitude wall-clock speedups over the vanilla solver on larger instances in some problem families, despite the runtime overhead of querying the model.

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