Open AccessBook
Constraint Processing
Reads0
Chats0
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
More filters
Journal Article
Constraint programming next challenge: Simplicity of use
TL;DR: ILOG's uniqueness, as far as CP is concerned, is that the research and development team that produces the CP products is also responsible for the development of the mathematical programming (MP) tool, namely ILOG CPLEX.
Book ChapterDOI
Constraint Programming Next Challenge: Simplicity of Use
TL;DR: The ILOG CPLEX tool as mentioned in this paper is a CP tool that is based on the ILOG research and development team that produces our CP products and is also responsible for the development of our mathematical programming (MP) tool.
Journal ArticleDOI
Structure identification of Boolean relations and plain bases for co-clones
TL;DR: This work gives a quadratic algorithm for the following structure identification problem: given a Boolean relation R and a finite set S of Boolean relations, can the relation R be expressed as a conjunctive query over the relations in the set S?
Proceedings ArticleDOI
A Dichotomy Theorem for the General Minimum Cost Homomorphism Problem
TL;DR: Gutin et al. as mentioned in this paper showed that the complexity of the minimum cost homomorphism problem can be studied by using algebraic methods similar to those used for constraint satisfaction problems.
Journal ArticleDOI
Dynamic Graphical Models
TL;DR: Solving difficult problems (such as deriving an approximation to an NP-complete optimization problem) might become worthwhile only because a solution can be applied many times for different problem instances.
References
More filters
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.