SBSAT: a State-Based, BDD-Based Satisfiability Solver

We present a new approach to SAT solvers, supporting efficient implementation of highly sophisticated search heuristics over a range of propositional inputs, including CNF formulas, but particularly BDDs. The approach memoizes search information using a new form of lookahead, called local-functioncomplete lookahead, during an extensive preprocessing phase; we expect to do further experimentation on heuristics made feasible by the memoization. Preprocessing also includes manipulating inputs to more exploitable form. This approach has been incorporated, along with existing tools such as lemmas, to build a SAT tool we call SBSAT. We show the feasibility of SBSAT by comparing it to zChaff on several of the benchmarks. We also show an interesting dependence of some standard benchmarks upon simply the independent/dependent variable distinction.