Abstract

Stochastic search has recently been shown to be successful for solving large boolean satisfiability problems. However, systematic methods tend to be more effective in problem domains with a large number of dependent variables: that is, variables whose truth values are directly determined by a smaller set of independent variables. In systematic search, truth values can be efficiently propagated from the independent to the dependent variables by unit propagation. Such propagation is more expensive in traditional stochastic procedures. In this paper we propose a mechanism for effectively dealing with dependent variables in stochastic search. We also provide empirical data showing the procedure outperforms the best previous stochastic and systematic search procedures on large formulas with a high ratio of dependent to independent variables. 1 Introduction Recent years have seen significant progress in our ability to solve large propositional satisfiability problems. Randomly generated pro...

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