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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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References
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Book ChapterDOI
A Unifying Framework for Tractable Constraints
TL;DR: All known classes with this property may be characterized by a simple algebraic closure condition, and this condition provides a uniform test to establish whether a given set of constraints falls into any of the known tractable classes, and may therefore be solved efficiently.
Proceedings Article
Constraint Satisfaction over Connected Row Convex Constraints.
TL;DR: It is established that path consistency over CRC constraints produces a minimal and decomposable network and is thus a polynomial-time decision procedure for CRC networks, and a new path-consistency algorithm for CRC constraints is presented.
Journal ArticleDOI
An empirical analysis of search in GSAT
Ian P. Gent,Toby Walsh +1 more
TL;DR: An extensive study of search in GSAT, an approximation procedure for propositional satisfiability, shows that when applied to randomly generated 3-SAT problems, there is a very simple scaling with problem size for both the mean number of satisfied clauses and the mean branching rate.
Journal ArticleDOI
Temporal query processing with indefinite information
TL;DR: This paper adopts Allen's influential interval algebra framework for representing temporal information and shows that when the representation language is sufficiently restricted it can develop efficient algorithms for answering interesting classes of queries.
Book ChapterDOI
A Hybrid Seachr Architecture Applied to Hard Random 3-SAT and Low-Autocorrelation Binary Sequences
TL;DR: In this article, the backtracking component of a backtracker can be used to improve the scalability of stochastic local search in a constrained space, cleanly combining local search with constraint programming techniques.