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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
Automated debugging based on a constraint model of the program and a test case
TL;DR: The model-based approach presented in this paper relies on a constraint representation of a program that is equivalent to the original program in terms of the input-output behavior under some reasonable assumptions and can be used for debugging smaller programs in less than 1 s.
Journal ArticleDOI
Maximizing Supermodular Functions on Product Lattices, with Application to Maximum Constraint Satisfaction
Andrei Krokhin,Benoit Larose +1 more
TL;DR: A large class of finite lattices is exhibited for which the problem of maximizing a supermodular function defined on a product of copies of a fixed finite lattice and given by an oracle can be solved in oracle-polynomial time.
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Advanced Methods for Decision Making and Risk Management in Sustainability Science
Jürgen P. Kropp,Jürgen Scheffran +1 more
TL;DR: In this article, a case-based reasoning system for supporting the modelling of estuaries is presented. But this system is not suitable for the modelling and explanation of sustainability indicators.
Proceedings Article
A first practical algorithm for high levels of relational consistency
Shant Karakashian,Robert J. Woodward,Christopher G. Reeson,Berthe Y. Choueiry,Christian Bessiere +4 more
TL;DR: It is empirically show that wR(*,m)C solves in a backtrack-free manner all the instances of some CSP benchmark classes, thus hinting at the tractability of those classes.
Journal ArticleDOI
On the conditional independence implication problem: A lattice-theoretic approach
TL;DR: In this paper, a lattice-theoretic framework that permits the study of the conditional independence (CI) implication problem relative to the class of discrete probability measures is presented, and a finite, sound and complete inference system relative to semi-lattice inclusions is presented.
References
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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.
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