On Inconsistent Clause-Subsets for Max-SAT Solving

Darras, Sylvain; Dequen, Gilles; Devendeville, Laure Brisoux; Li, Chu-Min

doi:10.1007/978-3-540-74970-7_18

Cited by 13 publications

(9 citation statements)

References 12 publications

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“…It proposes a new unsatisfiability-based algorithm which can be orders of magnitude more efficient that existing algorithms. Besides WBO, there are some other efficient Max-SAT solvers, such as SAT4J [SAT4J 2004], MaxSolver [Xing and Zhang 2005], IncWMaxSatz [Darras et al 2007], W-MaxSatz [Li et al 2006;Li et al 2009], and Clone [Pipatsrisawat and Darwiche 2007].…”

Section: Sat Solversmentioning

confidence: 99%

A SAT-based approach to cost-sensitive temporally expressive planning

Lü

Huang

Chen

et al. 2013

ACM Trans. Intell. Syst. Technol.

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Complex features, such as temporal dependencies and numerical cost constraints, are hallmarks of real-world planning problems. In this paper, we consider the challenging problem of cost-sensitive temporally expressive (CSTE) planning, which requires concurrency of durative actions and optimization of action costs. We first propose a scheme to translate a CSTE planning problem to a minimum cost (MinCost) satisfiability (SAT) problem and to integrate with a relaxed parallel planning semantics for handling true temporal expressiveness. Our scheme finds solution plans that optimize temporal makespan, and also minimize total action costs at the optimal makespan. We propose two approaches for solving MinCost SAT. The first is based on a transformation of a MinCost SAT problem to a weighted partial Max-SAT (WPMax-SAT), and the second, called BB-CDCL, is an integration of the branch-and-bound technique and the conflict driven clause learning (CDCL) method. We also develop a CSTE customized variable branching scheme for BB-CDCL which can significantly improve the search efficiency. Our experiments on the existing CSTE benchmark domains show that our planner compares favorably to the state-of-the-art temporally expressive planners in both efficiency and quality.

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Section: Sat Solversmentioning

confidence: 99%

A SAT-based approach to cost-sensitive temporally expressive planning

Lü

Huang

Chen

et al. 2013

ACM Trans. Intell. Syst. Technol.

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“…This can be improved by calculating an underestimation of the number of clauses which will become unsatisfied by extending the partial assignment to a complete assignment. Much progress has been made to compute good quality underestimations efficiently [6,4,7,3,8]. In this paper we present a generalized version of the algorithm in [7] which improves the quality of the underestimations.…”

Section: Introductionmentioning

confidence: 99%

Improved Exact Solver for the Weighted MAX-SAT Problem

Kügel

EPiC Series in Computing

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Many exact Max-SAT solvers use a branch and bound algorithm, where the lower bound is calculated with a combination of Max-SAT resolution and detection of disjoint inconsistent subformulas. We propose a propagation algorithm which improves the detection of disjoint inconsistent subformulas compared to algorithms previously used in Max-SAT solvers. We implemented this algorithm in our new solver akmaxsat and compared our solver with three solvers using unit propagation and restricted failed literal detection; these solvers are currently state-of-the-art on random Max-SAT instances. We also developed a lazy deletion data structure for our solver which speeds up lower bound calculation on instances with a high clauses-to-variables ratio. Our experiments show that our solver runs faster than the previously best solvers on randomly generated instances with a high clauses-to-variables ratio.

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“…Both components are applied at each node of the search space, and cooperate rather than work independently. Actually, in all the existing variants of MaxSatz [1,5,12,15,16] and in MiniMaxSat [8], the underestimation component guides the application of inference rules. More precisely, once a conflict is detected using unit propagation, MaxSAT resolution is only applied if the implication graph created by unit propagation contains certain structures.…”

Section: Introductionmentioning

confidence: 99%