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Learning Branching Heuristics for Propositional Model Counting
[article]
2020
arXiv
pre-print
Propositional model counting or #SAT is the problem of computing the number of satisfying assignments of a Boolean formula and many discrete probabilistic inference problems can be translated into a model counting problem to be solved by #SAT solvers. Generic "exact" #SAT solvers, however, are often not scalable to industrial-level instances. In this paper, we present Neuro#, an approach for learning branching heuristics for exact #SAT solvers via evolution strategies (ES) to reduce the number
arXiv:2007.03204v1
fatcat:uf562j3kozakrf3oxgvjsbtgqm