A Sharp Leap from Quantified Boolean Formula to Stochastic Boolean Satisfiability Solving

Stochastic Boolean Satisfiability (SSAT) is a powerful representation for the concise encoding of quantified decision problems with uncertainty. While it shares commonalities formula (QBF) satisfiability and has same PSPACE-complete complexity, SSAT solving tends to be more challenging as involves expensive model counting, a.k.a. Sharp-SAT. To date, solvers, especially those imposing no restrictions on quantification levels, remain much lacking. In this paper, we present new solver based framework clause selection cube distribution previously proposed QBF solving. With counting integrated learning techniques strengthened, our general effective. Experimental results demonstrate overall superiority algorithm in both performance memory usage compared state-of-the-art solvers number benchmark formulas.