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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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Journal Article
Técnicas de resolución de problemas de satisfacción de restricciones
Felip Manyà,Carla P. Gomes +1 more
TL;DR: Los problemas que se plantean en Inteligencia Artifgicial pueden formalizarse como un problema de satisfaccion de restricciones (CSP) y, luego, resolverse utilizando las tecnicas de resolucion que se han desarrollado para CSPs.
Proceedings Article
Local search for optimal global map generation using mid-decadal landsat images
TL;DR: This paper formulates a Global Map Generator problem as a Constraint Optimization Problem (GMG-COP) and describes an approach to solving It usmg local search and the integration of a GMG solver into a user interface for visualizing and comparing solutions.
Journal ArticleDOI
Local consistency for extended CSPs
TL;DR: Some rough principles for local consistency conditions in the extended framework of Constraint Satisfaction Problems are developed, appropriate notions of local consistency are formulated, and relationships between the various consistency conditions are established.
Journal ArticleDOI
Distributed constraint satisfaction with multiply sectioned constraint networks
TL;DR: A new algorithmic framework, multiply sectioned constraint networks (MSCNs), for solving distributed constraint satisfaction problems (DisCSPs) with complex local problems and it is shown that the MSCN-LJF algorithm is more efficient than junction tree-based DisCSP algorithms.
DissertationDOI
Data Editing and Logic: The covering set method from the perspective of logic
TL;DR: This thesis demonstrates that covering set correctibility is a strengthening of the well-known logical properties of soundness and refutation completeness, and gives the beginnings of a theoretical logical framework for error localisation, which might give new insights to the problem.
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.