A New Probabilistic Algorithm for Approximate Model Counting

Abstract

Constrained counting is important in domains ranging from artificial intelligence to software analysis. There are already a few approaches for counting models over various types of constraints. Recently, hashing-based approaches achieve success but still rely on solution enumeration. In this paper, a new probabilistic approximate model counter is proposed, which is also a hashing-based universal framework, but with only satisfiability queries. A dynamic stopping criteria, for the new algorithm, is presented, which has not been studied yet in previous works of hashing-based approaches. Although the new algorithm lacks theoretical guarantee, it works well in practice. Empirical evaluation over benchmarks on propositional logic formulas and SMT(BV) formulas shows that the approach is promising.