ALLQBF Solving by Computational Learning

  • Bernd Becker
  • Rüdiger Ehlers
  • Matthew Lewis
  • Paolo Marin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7561)


In the last years, search-based QBF solvers have become essential for many applications in the formal methods domain. The exploitation of their reasoning efficiency has however been restricted to applications in which a “satisfiable/unsatisfiable” answer or one model of an open quantified Boolean formula suffices as an outcome, whereas applications in which a compact representation of all models is required could not be tackled with QBF solvers so far.

In this paper, we describe how computational learning provides a remedy to this problem. Our algorithms employ a search-based QBF solver and learn the set of all models of a given open QBF problem in a CNF (conjunctive normal form), DNF (disjunctive normal form), or CDNF (conjunction of DNFs) representation. We evaluate our approach experimentally using benchmarks from synthesis of finite-state systems from temporal logic and monitor computation.


QBF Computational learning QBF model enumeration 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bernd Becker
    • 1
  • Rüdiger Ehlers
    • 2
  • Matthew Lewis
    • 1
  • Paolo Marin
    • 1
  1. 1.Albert-Ludwigs-Universität FreiburgGermany
  2. 2.Universität des SaarlandesGermany

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