Generating Satisfiable Problem Instances
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
BerkMin: A Fast and Robust Sat-Solver
Proceedings of the conference on Design, automation and test in Europe
Switching among Non-Weighting, Clause Weighting, and Variable Weighting in Local Search for SAT
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Hiding satisfying assignments: two are better than one
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
A simple model to generate hard satisfiable instances
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
An improved satisfiable SAT generator based on random subgraph isomorphism
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
Notes on generating satisfiable SAT instances using random subgraph isomorphism
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
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We report preliminary empirical results on Generating Satisfiable SAT instances using a variation of the Random Subgraph Isomorphism model. The experiments show that the model exhibits an easy-hard-easy pattern of empirical hardness. For both complete and incomplete solvers the hardness of the instances at the peak seems to increase exponentially with the instance size. The hardness of the instances generated by the model appears to be comparable with that of Quasigroup with Holes instances, known to be hard for Satisfiability solvers. A handful of state of the art SAT solvers we tested have different performances with respect to each other, when applied to these instances.