Unifying Decision and Function Queries in Stochastic Boolean Satisfiability

Abstract

Stochastic Boolean satisfiability (SSAT) is a natural formalism for optimization under uncertainty. Its decision version implicitly imposes a final threshold quantification on an SSAT formula. However, the single threshold quantification restricts the expressive power of SSAT. In this work, we enrich SSAT with an additional threshold quantifier, resulting in a new formalism SSAT(θ). The increased expressiveness allows SSAT(θ), which remains in the PSPACE complexity class, to subsume and encode the languages in the counting hierarchy. An SSAT(θ) solver, ClauSSat(θ), is developed. Experiments show the applicability of the solver in uniquely solving complex SSAT(θ) instances of parameter synthesis and SSAT extension.