Abstract

Stochastic local search (SLS) algorithms for propositional 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. 1

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