A PSPACE Subclass of Dependency Quantified Boolean Formulas and Its Effective Solving


  • Christoph Scholl Albert-Ludwigs-Universität Freiburg
  • Jie-Hong Roland Jiang National Taiwan University
  • Ralf Wimmer Albert-Ludwigs-Universität Freiburg
  • Aile Ge-Ernst Albert-Ludwigs-Universität Freiburg




Dependency quantified Boolean formulas (DQBFs) are a powerful formalism, which subsumes quantified Boolean formulas (QBFs) and allows an explicit specification of dependencies of existential variables on universal variables. This enables a succinct encoding of decision problems in the NEXPTIME complexity class. As solving general DQBFs is NEXPTIME complete, in contrast to the PSPACE completeness of QBF solving, characterizing DQBF subclasses of lower computational complexity allows their effective solving and is of practical importance.

Recently a DQBF proof calculus based on a notion of fork extension, in addition to resolution and universal reduction, was proposed by Rabe in 2017. We show that this calculus is in fact incomplete for general DQBFs, but complete for a subclass of DQBFs, where any two existential variables have either identical or disjoint dependency sets over the universal variables. We further characterize this DQBF subclass to be ΣP3 complete in the polynomial time hierarchy. Essentially using fork extension, a DQBF in this subclass can be converted to an equisatisfiable 3QBF with only a linear increase in formula size. We exploit this conversion for effective solving of this DQBF subclass and point out its potential as a general strategy for DQBF quantifier localization. Experimental results show that the method outperforms state-of-the-art DQBF solvers on a number of benchmarks, including the 2018 DQBF evaluation benchmarks.




How to Cite

Scholl, C., Roland Jiang, J.-H., Wimmer, R., & Ge-Ernst, A. (2019). A PSPACE Subclass of Dependency Quantified Boolean Formulas and Its Effective Solving. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 1584-1591. https://doi.org/10.1609/aaai.v33i01.33011584



AAAI Technical Track: Constraint Satisfaction and Optimization