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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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Proceedings Article
Incorporating Causal Prior Knowledge as Path-Constraints in Bayesian Networks and Maximal Ancestral Graphs
TL;DR: This work considers the formalisms of Causal Bayesian Networks and Maximal Ancestral Graphs and their Markov equivalence classes and introduces sound and complete procedures which are able to incorporate causal prior knowledge in such models.
Proceedings ArticleDOI
Distributed optimal planning: an approach by weighted automata calculus
Eric Fabre,Loïg Jezequel +1 more
TL;DR: It is shown that local plan selections can be performed by combining operations on weighted languages, and then a more practical implementation in terms of weighted automata calculus is proposed.
Proceedings Article
Symmetry breaking as a prelude to implied constraints: a constraint modelling pattern
TL;DR: It is demonstrated that the choice between alternative symmetry-breaking constraints is commonly made by considering either the amount of symmetry broken or the strength of pruning obtained in comparison with the overhead of enforcing the constraints.
Posted Content
Constraint Processing in Lifted Probabilistic Inference
Jacek Kisynski,David Poole +1 more
TL;DR: In this paper, the authors analyze lifted inference from the perspective of constraint processing and compare existing approaches and expose their advantages and limitations, showing that the wrong choice of a constraint processing method can lead to an exponential increase in computational complexity.
Journal ArticleDOI
A constraint satisfaction approach to resolving product configuration conflicts
Dong Yang,Ming Dong +1 more
TL;DR: In this paper, a method of applying constraint satisfaction to resolve product configuration conflicts is presented and the resolution to the configuration conflict problem can be regarded as solving a CSP.
References
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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.