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Constraint Processing
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TLDR
Rina Dechter synthesizes three decades of researchers work on constraint processing in AI, databases and programming languages, operations research, management science, and applied mathematics to provide the first comprehensive examination of the theory that underlies constraint processing algorithms.Abstract:
Constraint satisfaction is a simple but powerful tool. Constraints identify the impossible and reduce the realm of possibilities to effectively focus on the possible, allowing for a natural declarative formulation of what must be satisfied, without expressing how. The field of constraint reasoning has matured over the last three decades with contributions from a diverse community of researchers in artificial intelligence, databases and programming languages, operations research, management science, and applied mathematics. Today, constraint problems are used to model cognitive tasks in vision, language comprehension, default reasoning, diagnosis, scheduling, temporal and spatial reasoning.
In Constraint Processing, Rina Dechter, synthesizes these contributions, along with her own significant work, to provide the first comprehensive examination of the theory that underlies constraint processing algorithms. Throughout, she focuses on fundamental tools and principles, emphasizing the representation and analysis of algorithms.
·Examines the basic practical aspects of each topic and then tackles more advanced issues, including current research challenges
·Builds the reader's understanding with definitions, examples, theory, algorithms and complexity analysis
·Synthesizes three decades of researchers work on constraint processing in AI, databases and programming languages, operations research, management science, and applied mathematics
Table of Contents
Preface; Introduction; Constraint Networks; Consistency-Enforcing Algorithms: Constraint Propagation; Directional Consistency; General Search Strategies; General Search Strategies: Look-Back; Local Search Algorithms; Advanced Consistency Methods; Tree-Decomposition Methods; Hybrid of Search and Inference: Time-Space Trade-offs; Tractable Constraint Languages; Temporal Constraint Networks; Constraint Optimization; Probabilistic Networks; Constraint Logic Programming; Bibliographyread more
Citations
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Journal ArticleDOI
Variable ordering and constraint propagation for constrained CP-nets
Eisa Alanazi,Malek Mouhoub +1 more
TL;DR: This paper experimentally study the effect of variable ordering heuristics and constraint propagation when solving a constrained CP-net using a backtrack search algorithm, and investigates several look ahead strategies as well as the most constrained heuristic for variable ordering during search.
Journal ArticleDOI
Dynamic consistency of fuzzy conditional temporal problems
TL;DR: An algorithm is described which allows for testing if a CTPP is dynamically consistent, and a result is obtained by providing a polynomial mapping from STPPUs to CTPPs, showing that the former framework is at least as expressive as the second.
Journal ArticleDOI
Scheduling of maintenance work: A constraint-based approach
TL;DR: Within the framework of Knowledge Engineering, an application based on Constraints Satisfaction Problems (CSP) techniques, such as Forward checking, whereby, from a set of initial proposals, constraints are propagated until increasing better solutions are incrementally found is presented.
Proceedings ArticleDOI
An ant colony optimization approach to the traveling tournament problem
TL;DR: A new ant colony optimization approach is presented, hybridizing it with a forward checking and conflict-directed backjumping algorithm while using pattern matching and other constraint satisfaction strategies.
Proceedings ArticleDOI
Design validation of behavioral VHDL descriptions for arbitrary fault models
TL;DR: A flexible automatic test generation framework to detect a variety of design faults in systems with behavioral VHDL descriptions and an industrial CLP engine is used to solve it.
References
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Optimization by Simulated Annealing
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Computers and Intractability: A Guide to the Theory of NP-Completeness
TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
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Introduction to Algorithms
TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
Book
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.