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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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Book ChapterDOI
Computational Protein Design as a Cost Function Network Optimization Problem
David Allouche,Seydou Traoré,Isabelle André,Simon de Givry,George Katsirelos,Sophie Barbe,Thomas Schiex +6 more
TL;DR: This paper constructs different real CPD instances to evaluate CFN and 0/1LP algorithms as implemented in the toulbar2 and cplex solvers and observes that CFN algorithms bring important speedups compared to the CPD platform osprey but also to cplex.
Journal ArticleDOI
Cluster Tree Elimination for Distributed Constraint Optimization with Quality Guarantees
Ismel Brito,Pedro Meseguer +1 more
TL;DR: Combining cost function filtering with bound reasoning, the proposed DIMCTEf, an algorithm based on repeated executions of DMCTEF(r) with increasing r, which allows to alleviate their high size.
Proceedings Article
A BDD-based polytime algorithm for cost-bounded interactive configuration
TL;DR: This paper addresses the problem of extending configurators so that a user can interactively limit configuration choices based on a maximum cost of any valid configuration, in a complete, backtrack-free and real-time manner.
Journal ArticleDOI
Artificial Intelligence: From programs to solvers
TL;DR: The problem of generality in AI is revisited, the way in which this ’Models and Solvers’ agenda addresses the problem is looked at, and the relevance of this agenda to the grand AI goal of a computational account of intelligence and human cognition is discussed.
Combining Procedural and Declarative Knowledge in a Graphical Architecture
TL;DR: Reengineering such a dual architecture on a common foundation of graphical models enables a better understanding of both the substantial commonalities between procedural and declarative memory and the subtle differences that endow each with its own special character.
References
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Computers and Intractability: A Guide to the Theory of NP-Completeness
TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
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Introduction to Algorithms
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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Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.