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New checker for constraint network solutions

Published: 29 March 2017 Publication History

Abstract

A large variety of real-world problems in artificial intelligence can be viewed as Constraint Satisfaction Problems CSPs, Max-CSP, Weighted CSP, and other extensions. All resolution methods generate a solution. But, in some cases, these solutions are not correct and it is necessary to specify the detail of the solution(assigned values for variables and violated constraints). In this paper, we propose a new checker that can be used, as reference, to verify any solution generated by any solvers of CSP, Max-CSP and WCSP. Noted that, the one of the most advantages of this checker that can specify, exactly, the violated constraints as well as variables and values that causes this violation. In this regard, A set of theorems, propositions, examples and experimental results are integrated and discussed in order to validate the performance of the proposed checker.

References

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Lecoutre, C.: instances aviable at: http://www.cril.univartois.fr/~lecoutre/benchmarks.html.
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Statistical results available at: http://www.cril.univartois.fr/PB12/results/globalbybench.php?idev=68&idcat=0.
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Obtained results of Sat4j PB 2012-05-28 (complete) available at http://www.cril.univartois.fr/PB12/results/solver.php?idev=68&idsolver=2301.
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Generated solution of queen5-5-4 by Sat4j PB 2012-05-28 available at: http://www.cril.univartois.fr/PB12/results/bench.php?idev=68&idbench=79656.
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Larrosa, J., Schiex T.: Solving weighted CSP by maintaining arc consistency. Artificial Intelligence. 159(1-2), 1--26(2004).
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Obtained results of SCIP spx SCIP 2.1.1. 4. with SoPlex 1.6.0.3 fixed (complete) available at http://www.cril.univartois.fr/PB12/results/solver.php?idev=68&idsolver=2300.
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Generated solution of 4wqueens by Sat4j PB 2012-05-28 available at: http://www.cril.univartois.fr/PB12/results/bench.php?idev=68&idbench=79349.
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Obtained results of AbsconMax 11 2 PC-D, available at http://www.cril.univartois.fr/CPAI08/results/solver.php?idev=16&idsolver=360.

Cited By

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  • (2022)An Optimized Gradient Dynamic-Neuro-Weighted-Fuzzy Clustering Method: Application in the Nutrition FieldInternational Journal of Fuzzy Systems10.1007/s40815-022-01358-024:8(3731-3744)Online publication date: 26-Sep-2022

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cover image ACM Other conferences
BDCA'17: Proceedings of the 2nd international Conference on Big Data, Cloud and Applications
March 2017
685 pages
ISBN:9781450348522
DOI:10.1145/3090354
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • Ministère de I'enseignement supérieur: Ministère de I'enseignement supérieur

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Association for Computing Machinery

New York, NY, United States

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Published: 29 March 2017

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  1. Check a solution
  2. Constraints networks
  3. Quadratic programming
  4. benchmark instances

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View all
  • (2022)An Optimized Gradient Dynamic-Neuro-Weighted-Fuzzy Clustering Method: Application in the Nutrition FieldInternational Journal of Fuzzy Systems10.1007/s40815-022-01358-024:8(3731-3744)Online publication date: 26-Sep-2022

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