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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
Citations
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References
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Proceedings Article
Cyclic-clustering: a compromise between tree-clustering and cycle-cutset method for improving search efficiency
TL;DR: An automatic speed ratio control system of the integration servo mechanism type for a stepless transmission of an automotive vehicle that includes means for controlling a gain of the alteration rate of the speed ratio in response to a rotational speed of an output shaft of the Stepless transmission.
Book ChapterDOI
Basic Operators for Solving Constraints via Collaboration of Solvers
Carlos Castro,Eric Monfroy +1 more
TL;DR: In this article, the authors propose a strategy language for designing schemes of constraint solver collaborations, and exemplify the use of this language by describing some well known techniques for solving constraints over finite domains and non-linear constraints over real numbers via collaboration of solvers.
Book ChapterDOI
Derivation of constraints and database relations
TL;DR: It is shown that, given a set of relations R, it is possible to derive any other relation which is invariant under P, using only the projection, Cartesian product, and selection operators, together with the effectivedomain of R, provided that the effective domain contains at least three elements.
Book ChapterDOI
New Tractable Classes from Old
TL;DR: This paper introduces a method of combining two or more tractable classes over disjoint domains, in order to synthesise larger, more expressive tractable Classes, and demonstrates that the classes obtained are genuinely novel, and have not been previously identified.