A Structure-preserving Clause Form Translation
Journal of Symbolic Computation
Resolution for quantified Boolean formulas
Information and Computation
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
Beyond NP: the QSAT phase transition
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
A Distributed Algorithm to Evaluate Quantified Boolean Formulae
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
QUBE: A System for Deciding Quantified Boolean Formulas Satisfiability
IJCAR '01 Proceedings of the First International Joint Conference on Automated Reasoning
Evaluating Search Heuristics and Optimization Techniques in Propositional Satisfiability
IJCAR '01 Proceedings of the First International Joint Conference on Automated Reasoning
Improvements to the evaluation of quantified boolean formulae
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Backjumping for quantified Boolean logic satisfiability
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
The second QBF solvers comparative evaluation
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
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Trivial truth and backjumping are two optimization techniques that have been proposed for deciding quantified boolean formulas (QBFs) satisfiability. Both these techniques can greatly improve the overall performance of a QBF solver, but they are the expression of opposite philosophies. On one hand, trivial truth is a "look-ahead" policy: it is applied when descending the search tree to (try to) prune it. On the other hand, backjumping is a "look-back" policy: it is applied when backtracking to (try to) avoid useless explorations. Neither of these optimizations subsumes the other: it is easy to come up with examples in which trivial truth behaves much better than backjumping, and the other way around. In this paper we experimentally evaluate these two optimizations both on randomly generated and on real world test cases.