From Backtracking To Deep Learning: A Survey On Methods For Solving Constraint Satisfaction Problems
Constraint Satisfaction Problems (CSP) are a fundamental mechanism in artificial intelligence, but finding a solution is an NP-complete problem, requiring the exploration of a vast number of combinations to satisfy all constraints. To address this, extensive research has been conducted, leading to...
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| Format: | Article |
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Institute of Technology and Education Galileo da Amazônia
2025-01-01
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| Series: | ITEGAM-JETIA |
| Online Access: | http://itegam-jetia.org/journal/index.php/jetia/article/view/1449 |
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| author | Fatima AIT HATRIT Kamal AMROUN |
| author_facet | Fatima AIT HATRIT Kamal AMROUN |
| author_sort | Fatima AIT HATRIT |
| collection | DOAJ |
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Constraint Satisfaction Problems (CSP) are a fundamental mechanism in artificial intelligence, but finding a solution is an NP-complete problem, requiring the exploration of a vast number of combinations to satisfy all constraints. To address this, extensive research has been conducted, leading to the development of effective techniques and algorithms for different types of CSPs, ranging from exhaustive search methods, which explore the entire search space, to modern techniques that use deep learning to learn how to solve CSPs. This paper represents a descriptive and synthetic overview of various CSPs solving methods, organized by approach: systematic search methods, inference and filtering methods, structural decomposition methods, local search-based methods, and deep learning-based methods. By offering this structured classification, it presents a clear view of resolution strategies, from the oldest to the most recent, highlighting current trends and future challenges, there by facilitating the understanding and application of available approaches in the field.
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| format | Article |
| id | doaj-art-31d6add0094446b39e16d29901b18b1f |
| institution | Kabale University |
| issn | 2447-0228 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Institute of Technology and Education Galileo da Amazônia |
| record_format | Article |
| series | ITEGAM-JETIA |
| spelling | doaj-art-31d6add0094446b39e16d29901b18b1f2025-02-06T23:51:49ZengInstitute of Technology and Education Galileo da AmazôniaITEGAM-JETIA2447-02282025-01-01115110.5935/jetia.v11i51.1449From Backtracking To Deep Learning: A Survey On Methods For Solving Constraint Satisfaction ProblemsFatima AIT HATRIT0Kamal AMROUN1Université de Bejaia, Faculté des Sciences Exactes, Laboratoire d'Informatique Médicale et des Environnements Dynamiques et intelligents (LIMED)Université de Bejaia, Faculté des Sciences Exactes, Laboratoire d'Informatique Médicale et des Environnements Dynamiques et intelligents (LIMED), 06000 Bejaia, Algérie Constraint Satisfaction Problems (CSP) are a fundamental mechanism in artificial intelligence, but finding a solution is an NP-complete problem, requiring the exploration of a vast number of combinations to satisfy all constraints. To address this, extensive research has been conducted, leading to the development of effective techniques and algorithms for different types of CSPs, ranging from exhaustive search methods, which explore the entire search space, to modern techniques that use deep learning to learn how to solve CSPs. This paper represents a descriptive and synthetic overview of various CSPs solving methods, organized by approach: systematic search methods, inference and filtering methods, structural decomposition methods, local search-based methods, and deep learning-based methods. By offering this structured classification, it presents a clear view of resolution strategies, from the oldest to the most recent, highlighting current trends and future challenges, there by facilitating the understanding and application of available approaches in the field. http://itegam-jetia.org/journal/index.php/jetia/article/view/1449 |
| spellingShingle | Fatima AIT HATRIT Kamal AMROUN From Backtracking To Deep Learning: A Survey On Methods For Solving Constraint Satisfaction Problems ITEGAM-JETIA |
| title | From Backtracking To Deep Learning: A Survey On Methods For Solving Constraint Satisfaction Problems |
| title_full | From Backtracking To Deep Learning: A Survey On Methods For Solving Constraint Satisfaction Problems |
| title_fullStr | From Backtracking To Deep Learning: A Survey On Methods For Solving Constraint Satisfaction Problems |
| title_full_unstemmed | From Backtracking To Deep Learning: A Survey On Methods For Solving Constraint Satisfaction Problems |
| title_short | From Backtracking To Deep Learning: A Survey On Methods For Solving Constraint Satisfaction Problems |
| title_sort | from backtracking to deep learning a survey on methods for solving constraint satisfaction problems |
| url | http://itegam-jetia.org/journal/index.php/jetia/article/view/1449 |
| work_keys_str_mv | AT fatimaaithatrit frombacktrackingtodeeplearningasurveyonmethodsforsolvingconstraintsatisfactionproblems AT kamalamroun frombacktrackingtodeeplearningasurveyonmethodsforsolvingconstraintsatisfactionproblems |