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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 ArticleDOI
Constraint Programming Approach to Reconfigurable Processor Extension Generation and Application Compilation
TL;DR: The approach for automatic selection and synthesis of processor extensions as well as efficient application compilation for these newly generated extensions is discussed, implemented in the integrated design framework, IFPEC, built using Constraint Programming (CP).
Journal ArticleDOI
Strategies for designing distributed systems: case studies in the design of an air traffic management system
TL;DR: An abstract characterization of a number of different conceptual approaches to deal with cognitive complexity and redundancy in the National Airspace System are provided and their effectiveness is illustrated and evaluated in terms of concrete realizations found within the Nationalairspace System.
Proceedings Article
Randomized adaptive spatial decoupling for large-scale vehicle routing with time windows
TL;DR: Experimental results on hard instances with 1,000 customers and 90 vehicles show that the proposed randomized adaptive spatial decoupling (RASD) scheme, together with large neighborhood search, significantly improves the quality of the solutions under time constraints.
Book ChapterDOI
A Soft Constraint-Based Approach to QoS-Aware Service Selection
TL;DR: This work applies soft constraints to model SLAs and to decide how to rebuild compositions which may not satisfy all the requirements, in order not to completely stop running systems.
Proceedings ArticleDOI
Towards a unifying characterization for quantifying weak coupling in dec-POMDPs
TL;DR: A unified characterization of weak coupling is presented that brings together three complementary aspects of agent interaction structure that derive new bounds on the computational complexity of optimal Dec-POMDP planning.
References
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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.