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Constraint Processing

Rina Dechter
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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; Bibliography

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References
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Journal ArticleDOI

On the Complexity of Timetable and Multicommodity Flow Problems

TL;DR: A very primitive version of Gotlieb’s timetable problem is shown to be NP-complete, and therefore all the common timetable problems areNP-complete.
Journal ArticleDOI

Algorithms for constraint-satisfaction problems: a survey

Vipin Kumar
- 01 Apr 1992 - 
TL;DR: A large number of problems in AI and other areas of computer science can be viewed as special cases of the constraint-satisfaction problem, and a number of different approaches have been developed for solving them.
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Gröbner Bases: A Computational Approach to Commutative Algebra

TL;DR: This chapter discusses linear algebra in Residue Class Rings in Vector Spaces and Modules, and first applications of Gr bner Bases.
Proceedings Article

Hard and easy distributions of SAT problems

TL;DR: It is shown that by using the right distribution of instances, and appropriate parameter values, it is possible to generate random formulas that are hard, that is, for which satisfiability testing is quite difficult.
Journal ArticleDOI

Simple linear-time algorithms to test chordality of graphs, test acyclicity of hypergraphs, and selectively reduce acyclic hypergraphs

TL;DR: An article of golfing equipment has a golf tee attached to a spring-biassed reel by a length of string which can be aligned with the green or hole and used as an aid in swinging the club face in the correct direction.