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Single-Solver Algorithms for 2QBF

(Poster Presentation)
  • Sam Bayless
  • Alan J. Hu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7317)

Abstract

2QBF is a restriction of QBF, in which at most one quantifier alternation is allowed. This simplifying assumption makes the problem easier to reason about, and allows for simpler unit propagation and clause/cube learning procedures.We introduce two new 2QBF algorithms that take advantage of 2QBF specifically. The first improves upon earlier work by Ranjan, Tang, and Malik (2004), while the second introduces a new ‘free’ decision heuristic that doesn’t need to respect quantifier order. Implementations of both new algorithms perform better than two state-of-the-art general QBF solvers on formal verification and AI planning instances.

Keywords

Decision Variable Unit Propagation Decision Level Boolean Formula Boolean Modeling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Zhang, L., Malik, S.: Towards a Symmetric Treatment of Satisfaction and Conflicts in Quantified Boolean Formula Evaluation. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, pp. 185–199. Springer, Heidelberg (2002)Google Scholar
  2. 2.
    Zhang, L., Madigan, C., Moskewicz, M., Malik, S.: Efficient conflict-driven learning in a boolean satisfiability solver. In: Proceedings of the 2001 IEEE/ACM International Conference on Computer-Aided Design, pp. 279–285. IEEE Press (2001)Google Scholar
  3. 3.
    Prasad, M., Biere, A., Gupta, A.: A survey of recent advances in SAT-based formal verification. International Journal on Software Tools for Technology Transfer (STTT) 7(2), 156–173 (2005)Google Scholar
  4. 4.
    Ranjan, D., Tang, D., Malik, S.: A comparative study of 2QBF algorithms. In: The Seventh International Conference on Theory and Applications of Satisfiability Testing, SAT 2004 (2004)Google Scholar
  5. 5.
    Lonsing, F., Biere, A.: DepQBF: A dependency-aware QBF solver (system description). JSAT 7, 71–76 (2010)Google Scholar
  6. 6.
    Giunchiglia, E., Marin, P., Narizzano, M.: QuBE7.0 system description. Journal on Satisfiability, Boolean Modeling and Computation 7, 83–88 (2010)Google Scholar
  7. 7.
    Giunchiglia, E., Marin, P., Narizzano, M.: sQueezeBF: An Effective Preprocessor for QBFs Based on Equivalence Reasoning. In: Strichman, O., Szeider, S. (eds.) SAT 2010. LNCS, vol. 6175, pp. 85–98. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sam Bayless
    • 1
  • Alan J. Hu
    • 1
  1. 1.University of British ColumbiaVancouverCanada

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