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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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Dissertation
Tuning evolutionary search for closed-loop optimization
TL;DR: This investigation reveals that res sourcing issues affect optimization in general, and that clear patterns emerge relating specific properties of the different resourcing issues to performance effects.
Proceedings ArticleDOI
On Minimum-Redundancy Fix-Free Codes
TL;DR: The design of minimum-redundancy fix-free codes is an example of a constraint processing problem, and the first approach to constructing them is offered and a variation with an additional symmetry requirement is introduced.
Book ChapterDOI
Efficient Approximation Algorithms for Multi-objective Constraint Optimization
TL;DR: This approach builds upon recent advances in multi-objective heuristic search over weighted AND/OR search spaces and uses an e-dominance relation between cost vectors to significantly reduce the set of non-dominated solutions.
Proceedings ArticleDOI
Large hinge width on sparse random hypergraphs
TL;DR: It is proved that with high probability, hinge width on these sparse random hypergraphs can grow linearly with the expected number of hyperedges, and some theoretical evidence for random instances around satisfiability thresholds to be hard for a standard hinge-decomposition based algorithm is provided.
Journal ArticleDOI
Conditional and composite temporal CSPs
Malek Mouhoub,Amrudee Sukpan +1 more
TL;DR: A unique temporal CSP framework including numeric and symbolic temporal information, conditional constraints and composite variables, and two methods respectively based on Stochastic Local Search (SLS) and constraint propagation are presented.
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.