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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
Any time, complete algorithm for finding utilitarian optimal solutions to STPPs
Bart Peintner,Martha E. Pollack +1 more
TL;DR: Experimental results show that a single iteration of the greedy algorithm produces high-quality solutions, and the complete, memory-boundable algorithm has compelling anylime properties and outperforms a branch-and-bound algorithm.
Proceedings Article
H-DPOP: Using Hard Constraints to Prune the Search Space
TL;DR: H-DPOP is introduced, a hybrid algorithm that is b sed on DPOP and uses Constraint Decision Diagrams (CDDs) to rule out infeasible combinations, and thus compactly represent UTIL messa g .
Book ChapterDOI
Adaptive Autonomous Systems – From the System’s Architecture to Testing
TL;DR: A control architecture that combines a the sense-plan-act paradigm and model-based reasoning is presented, which is used to identify internal faults.
Practical tractability of csps by higher level consistency and tree decomposition
TL;DR: This thesis proposes new local consistency properties and algorithms for enforcing them without adding constraints or altering the network's topology, and theoretically characterize and empirically evaluate the proposed consistency properties on benchmark problems.
Journal ArticleDOI
Combining slicing and constraint solving for better debugging: the CONBAS approach
Birgit Hofer,Franz Wotawa +1 more
TL;DR: An approach that combines an advanced dynamic slicing method with constraint solving in order to reduce the number of delivered fault candidates and is called Constraints Based Slicing (Conbas).
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.
Book
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.