On the run-time behaviour of stochastic local search algorithms for SAT
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
A Lagrangian reconstruction of GENET
Artificial Intelligence
SAT-Encodings, Search Space Structure, and Local Search Performance
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Solving the Round Robin Problem Using Propositional Logic
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Capturing Structure with Satisfiability
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Scaling and Probabilistic Smoothing: Efficient Dynamic Local Search for SAT
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Recovering and Exploiting Structural Knowledge from CNF Formulas
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Journal of Symbolic Computation
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Additive versus multiplicative clause weighting for SAT
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Solving non-Boolean satisfiability problems with stochastic local search
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Balance and filtering in structured satisfiable problems
IJCAI'01 Proceedings of the 17th international joint conference on Artificial 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
Modelling and solving temporal reasoning as propositional satisfiability
Artificial Intelligence
Building structure into local search for SAT
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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Recent research has focused on bridging the gap between the satisfiability (SAT) and constraint satisfaction problem (CSP) formalisms. One approach has been to develop a many-valued SAT formula (MV-SAT) as an intermediate paradigm between SAT and CSP, and then to translate existing highly efficient SAT solvers to the MV-SAT domain. Experimental results have shown this approach can achieve significant improvements in performance compared with the traditional SAT and CSP approaches. In this paper, we follow a different route, developing SAT solvers that can automatically recognise CSP structure hidden in SAT encodings. This allows us to look more closely at how constraint weighting can be implemented in the SAT and CSP domains. Our experimental results show that a SAT-based approach to handle weights, together with CSP-based approach to variable instantiation, is superior to other combinations of SAT and CSP-based approaches. A further experiment on the round robin scheduling problem indicates that this many-valued constraint weighting approach outperforms other state-of-the-art solvers.