Using Boolean Constraint Propagation for Sub-clauses Deduction

Darras, Sylvain; Dequen, Gilles; Devendeville, Laure Brisoux; Mazure, Bertrand; Ostrowski, Richard; Saïs, Lakhdar

doi:10.1007/11564751_59

Cited by 8 publications

(4 citation statements)

References 4 publications

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“…Afin de réduire ce nombre, il est envisageable d'utiliser la détection de subsomptions [6], comme pour le problème SAT. Ce mécanisme peut s'appliquer à la simplification d'une instance en programmation entière 0/1 car la définition particulière des contraintes sur lesquelles nous travaillons le permet (clauses ne contenant que des littéraux positifs).…”

Section: Réduction D'une Instance Du Problème En Programmation Linéaireunclassified

Application du problème de caractérisation multiple à la conception de tests de diagnostic pour la biologie végétale

Chhel¹,

Lardeux²,

Saubion³

et al. 2013

Revue d'intelligence artificielle

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Section: Réduction D'une Instance Du Problème En Programmation Linéaireunclassified

Application du problème de caractérisation multiple à la conception de tests de diagnostic pour la biologie végétale

Chhel¹,

Lardeux²,

Saubion³

et al. 2013

Revue d'intelligence artificielle

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“…. , σ 7 where σ 1 = η[x 9 , c 7 , c 8 ] = (¬x 5 5 ∨ ¬x 7 5 ∨ ¬x 8 3 ) and σ 7 = (¬x 11 2 ∨ ¬x 12 3 ∨ ¬x 6 3 ∨ ¬x 1 5 ). The node x 1 corresponding to the asserting literal ¬x 1 is called the first Unique Implication Point (First UIP).…”

Section: Definition 3 (Asserting Clause Derivationmentioning

confidence: 99%

“…In Darras et al [5], the authors proposed a preprocessing based on unit propagation for sub-clauses deduction. Considering the implication graph generated by the constraint propagation process as a resolution tree, the proposed approach deduces sub-clauses from the original formula.…”

Section: Related Workmentioning

confidence: 99%

Learning for Dynamic Subsumption

Hamadi

Jabbour

Saïs

2009

2009 21st IEEE International Conference on Tools With Artificial Intelligence

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This paper presents an original dynamic subsumption technique for Boolean CNF formulae. It exploits simple and sufficient conditions to detect, during conflict analysis, clauses from the formula that can be reduced by subsumption. During the learnt clause derivation, and at each step of the associated resolution process, checks for backward subsumption between the current resolvent and clauses from the original formula are efficiently performed. The resulting method allows the dynamic removal of literals from the original clauses. Experimental results show that the integration of our dynamic subsumption technique within the state-of-the-art SAT solvers Minisat and Rsat particularly benefits to crafted problems.

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“…The use of propagation techniques to obtain semantic information has already been used in other scenarios. For example, in [11] unit propagation was used to deduce sub-clauses from implication graphs, and also unit propagation was used in [14] to detect redundant clauses in SAT formulas.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of At-Most-One and Exactly-One Relations for Improved SAT Encodings of Pseudo-Boolean Constraints

Ansótegui

Bofill

Coll

et al. 2019

Lecture Notes in Computer Science

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Pseudo-Boolean (PB) constraints often have a critical role in constraint satisfaction and optimisation problems. Encoding PB constraints to SAT has proven to be an efficient approach in many applications, however care must be taken to encode them compactly and with good propagation properties. It has been shown that at-most-one (AMO) and exactly-one (EO) relations over subsets of the variables can be exploited in various encodings of PB constraints, improving their compactness and solving performance. In this paper we detect AMO and EO relations completely automatically and exploit them to improve SAT encodings that are based on Multi-Valued Decision Diagrams (MDDs). Our experiments show substantial reductions in encoding size and dramatic improvements in solving time thanks to automatic AMO and EO detection.

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Using Boolean Constraint Propagation for Sub-clauses Deduction

Cited by 8 publications

References 4 publications

Application du problème de caractérisation multiple à la conception de tests de diagnostic pour la biologie végétale

Application du problème de caractérisation multiple à la conception de tests de diagnostic pour la biologie végétale

Learning for Dynamic Subsumption

Automatic Detection of At-Most-One and Exactly-One Relations for Improved SAT Encodings of Pseudo-Boolean Constraints

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