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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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Proceedings Article
Conditional constraint satisfaction: logical foundations and complexity
TL;DR: An island of tractability for CCSPs is identified, by extending structural decomposition methods originally proposed for CSPs by establishing completeness results for the first and the second level of the polynomial hierarchy.
Journal ArticleDOI
The complexity of conservative valued CSPs
TL;DR: In this paper, the complexity of a general-valued constraint language with all possible unary cost functions has been studied and shown to be polynomial-time hard in the case of all possible cost functions.
On Virtual Evidence and Soft Evidence in Bayesian Networks
TL;DR: An in-depth description of virtual evidence and its application to Bayesian networks is provided, and it is concluded that while the form of soft evidence depends only on ratios of scores and not on the scores’ absolute value, the actual score values may be obtained from any information source so desired.
Book ChapterDOI
Counting-based look-ahead schemes for constraint satisfaction
TL;DR: This paper investigates a recent partition-based approximation of tree-clustering algorithms, Iterative Join-Graph Propagation (IJGP), which belongs to the class of belief propagation algorithms that attracted substantial interest due to their success for probabilistic inference.
Proceedings Article
Modeling and computation in planning: better heuristics from more expressive languages
Guillem Francès,Hector Geffner +1 more
TL;DR: This work shows that the direct generalization of relaxed planning graph heuristics to more expressive languages that implicitly allow conjunctions of atoms with more than one state variable leaves open a crisp gap, as it fails to properly account for the constraints over these variables.
References
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Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Book
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.
Book
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.
Book
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.