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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
On the modelling and optimization of preferences in constraint-based temporal reasoning
TL;DR: It is shown that for a broad family of optimization criteria, the VDTP can express the same solution space as the DTPP, under the assumption of arbitrary piecewise-constant preference functions, and empirical results show that an implementation of this approach consistently outperforms prior algorithms by orders of magnitude.
Book ChapterDOI
Using hajós' construction to generate hard graph 3-colorability instances
Sheng Liu,Jian Zhang +1 more
TL;DR: A constructive algorithm using constraint propagation to generate 4-critical graph units (4-CGUs) which have only one triangle as subgraph and experiments show that these graphs are exceptionally hard for backtracking algorithms adopting Brelaz's heuristics.
Proceedings ArticleDOI
Modelling Grammar Constraints with Answer Set Programming.
Christian Drescher,Toby Walsh +1 more
TL;DR: This paper presents answer set programming (ASP) models for an important and very general class of constraints, including all constraints specified via grammars or automata that recognise some formal language.
Proceedings ArticleDOI
Integrating Time and Resources into Planning
Filip Dvorak,Roman Barták +1 more
TL;DR: A suboptimal domain-independent planning system Filuta that focuses on planning, where explicit time plays a major role and resources are constrained, is proposed.
Exploiting graph cutsets for sampling-based approximations in bayesian networks
Rina Dechter,Bozhena Bidyuk +1 more
TL;DR: This dissertation proposes a new algorithm for finding a minimum cost cut set of graph such that the complexity of exact reasoning is bounded when the cutset variables are assigned, and proposes an any-time bounds framework for computing bounds on posterior marginals.
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.