Learning Branching Heuristics for Propositional Model Counting

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We studied the feasibility of enhancing the variable branching heuristic in propositional model counting via learning

Abstract:

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 in...More

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