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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 Article
A scalable method for multiagent constraint optimization
Adrian Petcu,Boi Faltings +1 more
TL;DR: A new, complete method for distributed constraint optimization, based on dynamic programming, inspired by the sum-product algorithm, which is correct only for tree-shaped constraint networks is extended using a pseudotree arrangement of the problem graph.
Journal ArticleDOI
Potassco: The Potsdam Answer Set Solving Collection
Martin Gebser,Benjamin Kaufmann,Roland Kaminski,Max Ostrowski,Torsten Schaub,Marius Schneider +5 more
TL;DR: This paper gives an overview of the open source project Potassco, the Potsdam Answer Set Solving Collection, bundling tools for Answer Set Programming developed at the University ofPotsdam.
Journal ArticleDOI
A dichotomy theorem for constraint satisfaction problems on a 3-element set
TL;DR: Every subproblem of the CSP is either tractable or NP-complete, and the criterion separating them is that conjectured in Bulatov et al.
Proceedings Article
Conflict-driven answer set solving
TL;DR: A new approach to computing answer sets of logic programs, based on concepts from constraint processing (CSP) and satisfiability checking (SAT), to view inferences in answer set programming (ASP) as unit propagation on no-goods to provide a uniform constraint-based framework for the different kinds of inferences.
Journal ArticleDOI
Conflict-driven answer set solving: From theory to practice
TL;DR: An approach to computing answer sets of logic programs, based on concepts successfully applied in Satisfiability (SAT) checking, to view inferences in Answer Set Programming (ASP) as unit propagation on nogoods, and presents the first full-fledged algorithmic framework for native conflict-driven ASP solving.
References
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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.