C4.5: programs for machine learning
C4.5: programs for machine learning
A linear time algorithm for finding tree-decompositions of small treewidth
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Resolution for quantified Boolean formulas
Information and Computation
The nature of statistical learning theory
The nature of statistical learning theory
An algorithm to evaluate quantified Boolean formulae
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A comparison of structural CSP decomposition methods
Artificial Intelligence
Resolution versus Search: Two Strategies for SAT
Journal of Automated Reasoning
Solving Advanced Reasoning Tasks Using Quantified Boolean Formulas
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Validating the result of a Quantified Boolean Formula (QBF) solver: theory and practice
Proceedings of the 2005 Asia and South Pacific Design Automation Conference
Fixed-Parameter Hierarchies inside PSPACE
LICS '06 Proceedings of the 21st Annual IEEE Symposium on Logic in Computer Science
Data Mining
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Clause/term resolution and learning in the evaluation of quantified Boolean formulas
Journal of Artificial Intelligence Research
Backjumping for quantified Boolean logic satisfiability
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
The complexity of quantified constraint satisfaction problems under structural restrictions
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
sKizzo: a suite to evaluate and certify QBFs
CADE' 20 Proceedings of the 20th international conference on Automated Deduction
CSL'05 Proceedings of the 19th international conference on Computer Science Logic
Binary clause reasoning in QBF
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
Validating QBF validity in HOL4
ITP'11 Proceedings of the Second international conference on Interactive theorem proving
Validating QBF invalidity in HOL4
ITP'10 Proceedings of the First international conference on Interactive Theorem Proving
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In this paper we study the problem of integrating deduction and search with the aid of machine learning techniques to yield practically efficient decision procedures for quantified Boolean formulas (QBFs). We show that effective on-line policies can be learned from the observed performances of deduction and search on representative sets of formulas. Such policies can be leveraged to switch between deduction and search during the solving process. We provide empirical evidence that learned policies perform better than either deduction and search, even when the latter are combined using hand-made policies based on previous works. The fact that even with a proof-of-concept implementation, our approach is competitive with sophisticated state-of-the-art QBF solvers shows the potential of machine learning techniques in the integration of different reasoning methods.