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
Automated theorem proving: A logical basis (Fundamental studies in computer science)
Automated theorem proving: A logical basis (Fundamental studies in computer science)
Exact Max-SAT solvers for over-constrained problems
Journal of Heuristics
Artificial Intelligence
Study of lower bound functions for MAX-2-SAT
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
New inference rules for efficient Max-SAT solving
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Detecting disjoint inconsistent subformulas for computing lower bounds for Max-SAT
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Resolution in Max-SAT and its relation to local consistency in weighted CSPs
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
MaxSolver: An efficient exact algorithm for (weighted) maximum satisfiability
Artificial Intelligence
MiniMaxSAT: a new weighted Max-SAT solver
SAT'07 Proceedings of the 10th international conference on Theory and applications of satisfiability testing
Local search algorithms for partial MAXSAT
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Solving over-constrained problems with SAT technology
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Improved exact solvers for weighted Max-SAT
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
A complete calculus for Max-SAT
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
On solving the partial MAX-SAT problem
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
Solving (Weighted) Partial MaxSAT through Satisfiability Testing
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Within-problem learning for efficient lower bound computation in Max-SAT solving
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Personalisation of telecommunications services as combinatorial optimisation
IAAI'08 Proceedings of the 20th national conference on Innovative applications of artificial intelligence - Volume 3
New inference rules for Max-SAT
Journal of Artificial Intelligence Research
MINIMAXSAT: an efficient weighted max-SAT solver
Journal of Artificial Intelligence Research
Clone: solving weighted Max-SAT in a reduced search space
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Modelling Max-CSP as partial Max-SAT
SAT'08 Proceedings of the 11th international conference on Theory and applications of satisfiability testing
A preprocessor for Max-SAT solvers
SAT'08 Proceedings of the 11th international conference on Theory and applications of satisfiability testing
Max-SAT formalisms with hard and soft constraints
AI Communications
Restoring CSP Satisfiability with MaxSAT
Fundamenta Informaticae - RCRA 2009 Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion
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We describe three original exact solvers for Partial Max-SAT: PMS, PMS-hard, and PMS-learning. PMS is a branch and bound solver which incorporates efficient data structures, a dynamic variable selection heuristic, inference rules which exploit the fact that some clauses are hard, and a good quality lower bound based on unit propagation. PMS-hard is built on top of PMS and incorporates clause learning only for hard clauses; this learning is similar to the learning incorporated into modern SAT solvers. PMS-learning is built on top of PMS-hard and incorporates learning on both hard and soft clauses; the learning on soft clauses is quite different from the learning on SAT since it has to use Max-SAT resolution instead of SAT resolution. Finally, we report on the experimental investigation in which we compare the state-of-the-art solvers Toolbar and ChaffBS with PMS, PMS-hard, and PMS-learning. The results obtained provide empirical evidence that Partial Max-SAT is a suitable formalism for representing and solving over-constrained problems, and that the learning techniques we have defined in this paper can give rise to substantial performance improvements.