Maintaining reversible DAC for Max-CSP
Artificial Intelligence
Directed Arc Consistency Preprocessing
Constraint Processing, Selected Papers
Exact Max-SAT solvers for over-constrained problems
Journal of Heuristics
Towards inferring protein interactions: challenges and solutions
EURASIP Journal on Applied Signal Processing
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
New inference rules for Max-SAT
Journal of Artificial Intelligence Research
Exploiting inference rules to compute lower bounds for MAX-SAT solving
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
In the quest of the best form of local consistency for weighted CSP
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
MaxSolver: An efficient exact algorithm for (weighted) maximum satisfiability
Artificial Intelligence
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
Improved exact solvers for weighted Max-SAT
SAT'05 Proceedings of the 8th 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
Exploiting Cycle Structures in Max-SAT
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Algorithms for Weighted Boolean Optimization
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
Resolution-based lower bounds in MaxSAT
Constraints
Parallel search for maximum satisfiability
AI Communications - 18th RCRA International Workshop on “Experimental evaluation of algorithms for solving problems with combinatorial explosion”
A SAT-based approach to cost-sensitive temporally expressive planning
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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Recent research has focused on using the power of look-ahead to speed up the resolution of the Max-SAT problem. Indeed, look-ahead techniques such as Unit Propagation (UP) allow to find conflicts and to quickly reach the upper bound in a Branch-and-Bound algorithm, reducing the search-space of the resolution. In previous works, the Max-SAT solvers maxsatz9 and maxsatz14 use unit propagation to compute, at each node of the branch and bound search-tree, disjoint inconsistent subsets of clauses in the current subformula to estimate the minimum number of clauses that cannot be satisfied by any assignment extended from the current node. The same subsets may still be present in the subtrees, that is why we present in this paper a new method to memorize them and then spare their recomputation time. Furthermore, we propose a heuristic so that the memorized subsets of clauses induce an ordering among unit clauses to detect more inconsistent subsets of clauses. We show that this new approach improves maxsatz9 and maxsatz14 and suggest that the approach can also be used to improve other state-of-the-art Max-SAT solvers.