Treewidth: A Useful Marker of Empirical Hardness in Quantified Boolean Logic Encodings
LPAR '08 Proceedings of the 15th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning
A Compact Representation for Syntactic Dependencies in QBFs
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Efficiently Representing Existential Dependency Sets for Expansion-based QBF Solvers
Electronic Notes in Theoretical Computer Science (ENTCS)
Evaluating and certifying QBFs: A comparison of state-of-the-art tools
AI Communications
Hard QBF Encodings Made Easy: Dream or Reality?
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
Leveraging dominators for preprocessing QBF
Proceedings of the Conference on Design, Automation and Test in Europe
Exploiting structure in an AIG based QBF solver
Proceedings of the Conference on Design, Automation and Test in Europe
An Empirical Study of QBF Encodings: from Treewidth Estimation to Useful Preprocessing
Fundamenta Informaticae - RCRA 2008 Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion
Integrating dependency schemes in search-based QBF solvers
SAT'10 Proceedings of the 13th international conference on Theory and Applications of Satisfiability Testing
| Hi-index | 0.03 |
The best currently available solvers for quantified Boolean formulas (QBFs) process their input in prenex form, i.e., all the quantifiers have to appear in the prefix of the formula separated from the purely propositional part representing the matrix. However, in many QBFs derived from applications, the propositional part is intertwined with the quantifier structure. To tackle this problem, the standard approach is to convert such QBFs in prenex form, thereby losing structural information about the prefix. In the case of search-based solvers, the prenex-form conversion introduces additional constraints on the branching heuristic and reduces the benefits of the learning mechanisms. In this paper, we show that conversion to prenex form is not necessary: current search-based solvers can be naturally extended in order to handle nonprenex QBFs and to exploit the original quantifier structure. We highlight the two mentioned drawbacks of the conversion in prenex form with a simple example, and we show that our ideas can also be useful for solving QBFs in prenex form. To validate our claims, we implemented our ideas in the state-of-the-art search-based solver QuBE and conducted an extensive experimental analysis. The results show that very substantial speedups can be obtained