A Stochastic Non-CNF SAT Solver

  • Rafiq Muhammad
  • Peter J. Stuckey
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4099)


Stochastic local search techniques have been successful in solving propositional satisfiability (SAT) problems encoded in conjunctive normal form (CNF). Recently complete solvers have shown that there are advantages to tackling propositional satisfiability problems in a more expressive natural representation, since the conversion to CNF can lose problem structure and introduce significantly more variables to encode the problem. In this work we develop a non-CNF SAT solver based on stochastic local search techniques. Crucially the system must be able to represent how true a proposition is and how false it is, as opposed to the usual stochastic methods which represent simply truth or degree of falsity (penalty). Our preliminary experiments show that on certain benchmarks the non-CNF local search solver can outperform highly optimized CNF local search solvers as well as existing CNF and non-CNF complete solvers.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Rafiq Muhammad
    • 1
  • Peter J. Stuckey
    • 1
  1. 1.NICTA Victoria Laboratory, Department of Computer Science and Software EngineeringThe University of MelbourneAustralia

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