Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
PSATO: a distributed propositional prover and its application to quasigroup problems
Journal of Symbolic Computation - Special issue on parallel symbolic computation
Implementing the Davis–Putnam Method
Journal of Automated Reasoning
Local Search with Constraint Propagation and Conflict-Based Heuristics
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Combining local search and look-ahead for scheduling and constraint satisfaction problems
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Heuristics based on unit propagation for satisfiability problems
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Combining local search and backtracking techniques for constraint satisfaction
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
SAT-based verification of LTL formulas
FMICS'06/PDMC'06 Proceedings of the 11th international workshop, FMICS 2006 and 5th international workshop, PDMC conference on Formal methods: Applications and technology
Model checking with SAT-based characterization of ACTL formulas
ICFEM'07 Proceedings of the formal engineering methods 9th international conference on Formal methods and software engineering
Improving parallel local search for SAT
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
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We present a technique for checking instances of the satisfiability (SAT) problem based on a combination of the Davis-Putnam (DP) procedure and stochastic methods. We first use the DP procedure to some extent, so as to partition and reduce the search space. If the reduction does not lead to an answer, a stochastic algorithm is then used to search each subspace. This approach is proven to be efficient for several types of SAT instances. A parallel implementation of the method is described and some experimental results are reported.