Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Backbone guided local search for maximum satisfiability
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A backbone-search heuristic for efficient solving of hard 3-SAT formulae
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Backbones in optimization and approximation
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
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
On Computing Backbones of Propositional Theories
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Reasoning over biological networks using maximum satisfiability
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
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The Partial MAX-SAT Problem (PMSAT) is a variant of the MAX-SAT problem that consists of two CNF formulas defined over the same variable set. Its solution must satisfy all clauses of the first formula and as many clauses in the second formula as possible. This study is concerned with the PMSAT solution in setting a two-phase stochastic local search method that takes advantage of an estimated backbone variables of the problem. First experiments conducted on PMSAT instances derived from SAT instances indicate that this new method offers significant performance benefits over state-of-the-art PMSAT techniques.