Efficient local search for very large-scale satisfiability problems
ACM SIGART Bulletin
Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
KI '97 Proceedings of the 21st Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Automatic SAT-compilation of planning problems
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
When gravity fails: local search topology
Journal of Artificial Intelligence Research
Ten challenges in propositional reasoning and search
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
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
Evaluating las vegas algorithms: pitfalls and remedies
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A Hybrid Seachr Architecture Applied to Hard Random 3-SAT and Low-Autocorrelation Binary Sequences
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
Towards an efficient SAT encoding for temporal reasoning
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
The set of parameterized k-covers problem
Theoretical Computer Science
Programming for modular reconfigurable robots
Programming and Computing Software
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Stochastic local search (SLS) algorithms for prepositional satisfiability testing (SAT) have become popular and powerful tools for solving suitably encoded hard combinatorial from different domains like, e.g., planning. Consequently, there is a considerable interest in finding SAT-encodings which facilitate the efficient application of SLS algorithms. In this work, we study how two encodings schemes for combinatorial problems, like the well-known Constraint Satisfaction or Hamilton Circuit Problem, affect SLS performance on the SAT-encoded instances. To explain the observed performance differences, we identify features of the induces search spaces which affect SLS performance. We furthermore present initial results of a comparitive analysis of the performance of the SAT-encoding and-solving approach versus that of native SLS algorithms directly applied to the unencoded problem instances.