Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Efficient conflict driven learning in a boolean satisfiability solver
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
CAMA: A Multi-Valued Satisfiability Solver
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Exploiting multivalued knowledge in variable selection heuristics for SAT solvers
Annals of Mathematics and Artificial Intelligence
Compiling finite linear CSP into SAT
Constraints
Heuristics based on unit propagation for satisfiability problems
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Solving non-Boolean satisfiability problems with stochastic local search
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Propagation = lazy clause generation
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Nogood processing in csps
Mapping problems with finite-domain variables to problems with boolean variables
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
Exploiting short supports for generalised arc consistency for arbitrary constraints
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Resolution procedures for multiple-valued optimization
Information Sciences: an International Journal
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We present a new complete multi-valued SAT solver, based on current state-of-the-art SAT technology. It features watched literal propagation and conflict driven clause learning. We combine this technology with state-of-the-art CP methods for branching and introduce quantitative supports which augment the watched literal scheme with a watched domain size scheme. Most importantly, we adapt SAT nogood learning for the multi-valued case and demonstrate that exploiting the knowledge that each variable must take exactly one out of many values can lead to much stronger nogoods. Experimental results assess the benefits of these contributions and show that solving multi-valued SAT directly often works better than reducing multi-valued constraint problems to SAT.