CAMA: A Multi-Valued Satisfiability Solver

This paper presents the multi-valued SAT solver CAMA. CAMAgeneralizes the recently developed speed-up techniques usedin state-of-the-art binary SAT solvers, such as the two-literal-watching scheme for Boolean constraint propagation (BCP), conflict-based learning with identifying the first unique implication point (UIP), and non-chronological back-tracking. In addition, a novel minimum value set (MVS) technique is introduced for improving the efficiency of conflict-based learning. By analyzing the conflict clauses, MVS can potentially prune conflictingspace that has not been searched before. Two different decisionheuristics are discussed and evaluated. Finally the performanceof CAMA is compared with Chaff using on a one-hot-encodingscheme. The experimental results show that, for MV-SAT problemswith large variable domains, CAMA outperforms Chaff.