Dependency Learning for QBF

  • Tomáš Peitl
  • Friedrich Slivovsky
  • Stefan Szeider
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10491)


Quantified Boolean Formulas (QBFs) can be used to succinctly encode problems from domains such as formal verification, planning, and synthesis. One of the main approaches to QBF solving is Quantified Conflict Driven Clause Learning (QCDCL). By default, QCDCL assigns variables in the order of their appearance in the quantifier prefix so as to account for dependencies among variables. Dependency schemes can be used to relax this restriction and exploit independence among variables in certain cases, but only at the cost of nontrivial interferences with the proof system underlying QCDCL. We propose a new technique for exploiting variable independence within QCDCL that allows solvers to learn variable dependencies on the fly. The resulting version of QCDCL enjoys improved propagation and increased flexibility in choosing variables for branching while retaining ordinary (long-distance) Q-resolution as its underlying proof system. In experiments on standard benchmark sets, an implementation of this algorithm shows performance comparable to state-of-the-art QBF solvers.



The authors thank Florian Lonsing for helpful discussions related to QCDCL. This research was kindly supported by FWF grants P27721 and W1255-N23.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Tomáš Peitl
    • 1
  • Friedrich Slivovsky
    • 1
  • Stefan Szeider
    • 1
  1. 1.Algorithms and Complexity GroupTU WienViennaAustria

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