E. Giunchiglia, M. Narizzano, and A. Tacchella. Backjumping for quantified boolean logic satisfiability. Artif. Intell., 145(1-2):99--120, 2003. Home/Search Document Details and Download
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Qubos: Deciding Quantified Boolean Logic using Propositional.. - Ayari, Basin (2002) (Correct)
....extensions of the Davis Putnam method. Cadoli et al. s techniques were tuned for randomly generated problems and Rintanen s strategies were specially designed for planning problems whose quantifiers have a fixed ### structure. Other work includes that of Letz [10] and Giunchiglia et al. [7] who have generalized the backjumping heuristic (also called dependency directed backtracking) to QBL. Our approach di#ers from all of these in that it is not based on Davis Putnam, it can operate freely on subformulae of the input formula (this avoids a major source of ine#ciency of Davis Putnam ....
Enrico Giunchiglia, Massimo Narizzano, and Armando Tacchella. Backjumping for quantified boolean logic satisfiability. In Proceedings of the 17th International Conference on Artificial Intelligence (IJCAI-01), August 4--10 2001.
On Quantifier Shifting for Quantified Boolean Formulas - Egly, Tompits, Woltran (2002) (Correct)
.... formulas with a QBF solver has become an attractive and increasingly important research topic over the last years (cf. e.g. 12, 5, 4, 11] The QBFs resulting from the encodings are usually not in a specific normal form which prevents the application of most of the available QBF provers [9, 3, 6, 8, 10, 12] without a translation into normal form. The only kind of QBF solvers which can handle arbitrary formulas is based on binary decision diagrams (BDDs) In order to make more practicably successful QBF solvers available for solving the encoded problems, a transformation of an arbitrary QBF into a ....
E. Giunchiglia, M. Narizzano, and A. Tacchella. Backjumping for Quantified Boolean Logic Satisfiability. In Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI-01), 2001.
On the Effectiveness of Backjumping and Trivial.. - Giunchiglia.. (2001) Self-citation (Giunchiglia Narizzano Tacchella) (Correct)
....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. 1 Introduction Trivial truth [1, 2] and backjumping [3] 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 two opposite philosophies. On one hand, trivial truth is a ....
....CSP (see, e.g. 12] QUBE is as far as we know the first QBF solver to implement such technique. In addition to performing CBJ, QUBE also skips the other side of branches on universal literals that would lead again to an empty formula: this is called solution directed backjumping (SBJ) in [3] where such technique is introduced. In the following, we refer to the combination of CBJ and SBJ simply as backjumping . It turns out that, exactly as in SAT and CSP, backjumping has some overhead, but it can produce significant speed ups [3] Currently QUBE is distributed in two fashions: ....
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E. Giunchiglia, M. Narizzano, and A. Tacchella. Backjumping for Quantified Boolean Logic Satisfiability. In Proc. of IJCAI, 2001. To appear.
QUBE: A system for deciding Quantified Boolean.. - Giunchiglia.. (2001) (10 citations) Self-citation (Giunchiglia Narizzano Tacchella) (Correct)
....have the same behavior on QBFs without universal quantifiers. 4 QUBE options Consider Figure 1. QUBE ver. 1.0 features backjumping, trivial truth, six different branching heuristics, i.e. implementations of ChooseLiteral, and other control options. The backjumping procedure implemented in QUBE [7] is a generalization of the conflict direct backjumping procedure as implemented in SAT solvers. As far as we know, QUBE is the only QBF solver with backjumping. Because of the potential overhead, backjumping has to be enabled when compiling the system, while all the other heuristics and ....
Enrico Giunchiglia, Massimo Narizzano, and Armando Tacchella. Backjumping for quantified boolean logic satisfiability. In Proc. of the International Joint Conference on Artificial Intelligence (IJCAI'2001), 2001.
Finding Small Unsatisfiable Cores to Prove.. - Interian, Corvera.. (Correct)
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E. Giunchiglia, M. Narizzano, and A. Tacchella. Backjumping for quantified boolean logic satisfiability. Artif. Intell., 145(1-2):99--120, 2003.
Solution Learning and Solution Directed Backjumping, Revisited - Gent, Rowley (2004) (Correct)
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E. Giunchiglia, M. Narizzano, and A. Tacchella, `Backjumping for quantified boolean logic satisfiability', in IJCAI, pp. 275--281, (2001).
A Constraint-Propagation Approach to Quantified Constraints - Bordeaux (Correct)
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E. Giunchiglia, M. Narizzano, and A. Tacchella. Backjumping for quantified boolean logic satisfiability. In Proc. of the 17th Int. Joint Conf. on AI (IJCAI), pages 275--281, Seattle, 2001. Morgan Kaufmann.
Watching Clauses in Quantified Boolean Formulae - Andrew Rowley University (2003) (Correct)
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Giunchiglia, E., Narizzano, M., Tacchella, A.: Backjumping for quantified boolean logic satisfiability. In: IJCAI. (2001) 275--281
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