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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 Article
Explanations and Proof Trees
TL;DR: In this paper, a model for explanations in a set theoretical framework using the notions of closure or fixpoint is proposed, where sets of rules associated with monotonic operators allow to define proof trees.
Proceedings Article
Combining parallel search and parallel consistency in constraint programming
TL;DR: This paper investigates how to combine parallel search with parallel consistency and evaluates which problems are suitable and which are not, showing that parallelizing the entire solving process in constraint programming is a major challenge.
Journal ArticleDOI
Solving connected row convex constraints by variable elimination
TL;DR: This work introduces a novel variable elimination method to solve the constraints of connected row convex constraints and identifies several nice properties that enable the development of a fast composition algorithm whose complexity is linear to the size of the variable domains.
An Algorithm for Generating All Connected Subgraphs with k Vertices of a Graph
TL;DR: ConSubg(k;G) as mentioned in this paper is an algorithm for computing all the connected subgraphs of a xed size k of a graph G. The two main features of ConSubg are the construction of a combination tree and the denition of an operator t applied to the nodes of the tree that allow us to generate without duplication the connected subsets.
Journal ArticleDOI
Intelligent variable orderings and re-orderings in DAC-based solvers for WCSP
Federico Heras,Javier Larrosa +1 more
TL;DR: The effect of heuristic orders at three levels of increasing overhead is analyzed and it is found that inverse degree, sum of unary weights (dynamic order) and re-ordering with the sum ofunary weights are good heuristics which are always better than a random order.
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.