Abstract
Learning is an essential pruning technique in modern SAT solvers, but it exploits a relatively small amount of information that can be deduced from the conflicts. Recently a new pruning technique called supercubing was proposed by Goldberg et al. (2002). Supercubing can exploit functional symmetries that are abundant in industrial SAT instances. We point out the significant difficulties of integrating supercubing with learning and propose solutions. Our experimental solver is the first supercubing-based solver with performance comparable to leading edge solvers.
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