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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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Proceedings ArticleDOI
Multi-level Parallelism in the Computational Modeling of the Heart
Carolina Ribeiro Xavier,R. Sachetto,Vinícius da Fonseca Vieira,R. Weber dos Santos,Wagner Meira +4 more
TL;DR: This work proposes and implements a new parallel algorithm for the solution of cardiac models that uses both pipelining and data decomposition techniques and indicates that the proposed algorithm is able to increase the parallel efficiency up to 20% when compared to the traditional approach that uses pure data-level parallelism.
Book ChapterDOI
Cost propagation: numerical propagation for optimization problems
Birgit Grohe,Dag Wedelin +1 more
TL;DR: The experiments illustrate that even without a top level search, cost propagation can by itself solve non-trivial problems, and also be attractive compared to standard methods.
Journal ArticleDOI
Study of Global Change Impacts on the Inland Navigation Management: Application on the Nord-Pas de Calais Network
TL;DR: It appeared that the multi-scale modeling approach for inland navigation networks that was proposed during the last TRA Conference in Paris in 2014 is useful to determine the resilience of these networks and their ability to guarantee the navigation conditions during drought and flood periods.
Journal ArticleDOI
A Constraint-based Approach for Annotating Music Scores with Gestural Information
TL;DR: The problem representation, the search strategy and a validation of the model against human performance are illustrated and it is shown that this model is suitable to be modeled via a constraint-based approach, coupled with a strategy aimed at maximizing the gestural comfort of performers.
Journal ArticleDOI
A modelling approach for dedicated machinery to support the effects of mass customisation
TL;DR: The approach presented in this paper employs the capability of a modelling environment to represent and simulate variations in machine design configuration and assess their ability to process variant products.
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.
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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.
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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.