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  1. Home > Articles & Issues >
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  3. New schemes for simp ...
Article

New schemes for simplifying binary constraint satisfaction problems

Wady Naanaa (1) (2) (3)
(1) Université de Monastir - University of Monastir
(2) جامعة المنستير - Université de Monastir - University of Monastir
(3) Université de Monastir - University of Monastir - جامعة المنستير
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Submitted on
March 22, 2018
Accepted on
February 9, 2020
Published on
June 4, 2020
Last modified on
March 31, 2025
Volume 22
vol. 22:1
Discrete Algorithms
DOI
10.23638/DMTCS-22-1-10
License
https://about.hal.science/hal-authorisation-v1
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New schemes for simplifying binary constraint satisfaction problems

Wady Naanaa (1) (2) (3)
(1) Université de Monastir - University of Monastir
(2) جامعة المنستير - Université de Monastir - University of Monastir
(3) Université de Monastir - University of Monastir - جامعة المنستير
Abstract
Finding a solution to a Constraint Satisfaction Problem (CSP) is known to be an NP-hard task. This has motivatedthe multitude of works that have been devoted to developing techniques that simplify CSP instances before or duringtheir resolution.The present work proposes rigidly enforced schemes for simplifying binary CSPs that allow the narrowing of valuedomains, either via value merging or via value suppression. The proposed schemes can be viewed as parametrizedgeneralizations of two widely studied CSP simplification techniques, namely, value merging and neighbourhoodsubstitutability. Besides, we show that both schemes may be strengthened in order to allow variable elimination,which may result in more significant simplifications. This work contributes also to the theory of tractable CSPs byidentifying a new tractable class of binary CSP.
Keywords
  • [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
  • [INFO.INFO-CC]Computer Science [cs]/Computational Complexity [cs.CC]
  • Constraint satisfaction problems
  • value merging
  • variable elimination
  • tractable CSP
Mathematics Subject Classification 2020
  • 68Q17 - Computational difficulty of problems (lower bounds, completeness, difficulty of approximation, etc.)
  • 68Q27 - Parameterized complexity, tractability and kernelization
  • 68T20 - Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
Linked publications - datasets - software
  • Has review

    Review available on zbMATH Open, by Silvija Vlah Jerić (Zagreb) (English)

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