CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
SAT problems with chains of dependent variables
Discrete Applied Mathematics - The renesse issue on satisfiability
Additive versus multiplicative clause weighting for SAT
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
A new method for solving hard satisfiability problems
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Evidence for invariants in local search
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Captain Jack: new variable selection heuristics in local search for SAT
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
EagleUP: solving random 3-SAT using SLS with unit propagation
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
Diversification and determinism in local search for satisfiability
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Random walk with continuously smoothed variable weights
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Improving stochastic local search for SAT with a new probability distribution
SAT'10 Proceedings of the 13th international conference on Theory and Applications of Satisfiability Testing
Dynamic scoring functions with variable expressions: new SLS methods for solving SAT
SAT'10 Proceedings of the 13th international conference on Theory and Applications of Satisfiability Testing
Satisfying versus falsifying in local search for satisfiability
SAT'12 Proceedings of the 15th international conference on Theory and Applications of Satisfiability Testing
A method to avoid duplicative flipping in local search for SAT
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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Intensification and diversification are the key factors that control the performance of stochastic local search in satisfiability (SAT). Recently, Novelty Walk has become a popular method for improving diversification of the search and so has been integrated in many well-known SAT solvers such as TNM and gNovelty+. In this paper, we introduce new heuristics to improve the effectiveness of Novelty Walk in terms of reducing search stagnation. In particular, we use weights (based on statistical information collected during the search) to focus the diversification phase onto specific areas of interest. With a given probability, we select the most frequently unsatisfied clause instead of a totally random one as Novelty Walk does. Amongst all the variables appearing in the selected clause, we then select the least flipped variable for the next move. Our experimental results show that the new weight-enhanced diversification method significantly improves the performance of gNovelty+ and thus outperforms other local search SAT solvers on a wide range of structured and random satisfiability benchmarks.