In this paper we present a new technique to solve Quantified Boolean Formulas (QBF). Our technique applies the idea of dynamic partitioning to QBF solvers. Dynamic partitioning has previously been utilized in #SAT solvers that count the number of models of a propositional formula. One of the main differences with the #SAT case comes from the solution learning techniques employed in search based QBF solvers. Extending solution learning to a partitioning solver involves some considerable complexities which we show how to resolve. We have implemented our ideas in a new QBF solver, and demonstrate that dynamic partitioning is able to increase the performance of search based solvers, sometimes significantly. Empirically our new solver offers performance that is superior to other search based solvers and in many cases superior to non-search based solvers.


Variable Versus Current Path Boolean Formula Propositional Formula Average Success Rate 
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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Horst Samulowitz
    • 1
  • Fahiem Bacchus
    • 1
  1. 1.Department of Computer Science, University of TorontoCanada

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