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Constraint Processing
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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
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References
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Book
Encyclopedia of Artificial Intelligence
TL;DR: This reference work compasses the variable approaches to Artificial Intelligence in 267 articles written by 205 experts and clarifies and corrects misinterpretations and provides a proper understanding of Artificial Intelligence.
BookDOI
Programming with constraints: an introduction
TL;DR: Part 1 Constraints: constraints simplifications, optimization and implication finite constraint domains, and other constraint programming languages.
Proceedings Article
Noise strategies for improving local search
TL;DR: It is shown that mixed random walk is the superior strategy for solving MAX-SAT problems, and results demonstrating the effectiveness of local search with walk for solving circuit synthesis and circuit diagnosis problems are presented.
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Optimization by Simulated Annealing: An Experimental Evaluation; Part II, Graph Coloring and Number Partitioning
TL;DR: This is the second in a series of three papers that empirically examine the competitiveness of simulated annealing in certain well-studied domains of combinatorial optimization.
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Rigorous Global Search: Continuous Problems
TL;DR: This work verifies the existence of non-Differentiable Problems in Software Environments and investigates the use of Intermediate Quantities in the Expression Values to Optimize the Solution.