ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Resolution for quantified Boolean formulas
Information and Computation
An Algorithm to Evaluate Quantified Boolean Formulae and Its Experimental Evaluation
Journal of Automated Reasoning
Lemma and Model Caching in Decision Procedures for Quantified Boolean Formulas
TABLEAUX '02 Proceedings of the International Conference on Automated Reasoning with Analytic Tableaux and Related Methods
Towards a Symmetric Treatment of Satisfaction and Conflicts in Quantified Boolean Formula Evaluation
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
QBF reasoning on real-world instances
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
A solver for quantified Boolean and linear constraints
Proceedings of the 2007 ACM symposium on Applied computing
Modeling adversary scheduling with QCSP+
Proceedings of the 2008 ACM symposium on Applied computing
Guiding Search in QCSP + with Back-Propagation
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
QuBIS: An (In)complete Solver for Quantified Boolean Formulas
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
A solver for QBFs in negation normal form
Constraints
Generalizing consistency and other constraint properties to quantified constraints
ACM Transactions on Computational Logic (TOCL)
A Solver for QBFs in Nonprenex Form
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Abstract branching for quantified formulas
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Solving QBF with combined conjunctive and disjunctive normal form
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
QCSP made practical by virtue of restricted quantification
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
A multi-engine solver for quantified boolean formulas
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Learning to integrate deduction and search in reasoning about quantified boolean formulas
FroCoS'09 Proceedings of the 7th international conference on Frontiers of combining systems
A non-prenex, non-clausal QBF solver with game-state learning
SAT'10 Proceedings of the 13th international conference on Theory and Applications of Satisfiability Testing
Solving quantified boolean formulas with circuit observability don't cares
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
QBF modeling: exploiting player symmetry for simplicity and efficiency
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
Unordered constraint satisfaction games
MFCS'12 Proceedings of the 37th international conference on Mathematical Foundations of Computer Science
Bridging the gap between dual propagation and CNF-based QBF solving
Proceedings of the Conference on Design, Automation and Test in Europe
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In recent years we have seen significant progress in the area of Boolean satisfiability (SAT) solving and its applications. As a new challenge, the community is now moving to investigate whether similar advances can be made in the use of Quantified Boolean Formulas (QBF). QBF provides a natural framework for capturing problem solving and planning in multi-agent settings. However, contrarily to single-agent planning, which can be effectively formulated as SAT, we show that a QBF approach to planning in a multi-agent setting leads to significant unexpected computational difficulties. We identify as a key difficulty of the QBF approach the fact that QBF solvers often end up exploring a much larger search space than the natural search space of the original problem. This is in contrast to the experience with SAT approaches. We also show how one can alleviate these problems by introducing two special QBF formulations and a new QBF solution strategy. We present experiments that show the effectiveness of our approach in terms of a significant improvement in performance compared to earlier work in this area. Our work also provides a general methodology for formulating adversarial scenarios in QBF.