Fast Converging Anytime Model Counting

Model counting is a fundamental problem which has been influential in many applications, from artificial intelligence to formal verification. Due the intrinsic hardness of model counting, approximate techniques have developed solve real-world instances counting. This paper designs new anytime approach called PartialKC for The idea form partial knowledge compilation provide an unbiased estimate count can converge exact count. Our empirical analysis demonstrates that achieves significant scalability and accuracy over prior state-of-the-art counters, including satss STS. Interestingly, results show reaches convergence therefore provides performance comparable counters.