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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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Book ChapterDOI
Theory and Techniques for Synthesizing Efficient Breadth-First Search Algorithms
TL;DR: A theory of efficient BFS (EBFS), along with a simple recursive program schema for carrying out the search, based on dominance relations, shows that greedy and greedy-like algorithms form a very useful and important sub-category of EBFS.
Journal ArticleDOI
FPT Algorithms for Diverse Collections of Hitting Sets
TL;DR: In this article, the authors study the d-Hitting Set and feedback vertex set problems through the paradigm of finding diverse collections of r solutions of size at most k each, which has recently been introduced to the field of parameterized complexity.
DissertationDOI
Online test paper generation for a web-based mathematics testing environment
TL;DR: This research aims to investigate different multiobjective optimization, data mining and probabilistic techniques for supporting the three components of the Web-based Mathematics testing environment, which consists of three major components, namely question item calibration, online test paper generation, and automatic solution assessment.
Proceedings Article
∃-STRIPS: existential quantification in planning and constraint satisfaction
Guillem Francès,Hector Geffner +1 more
TL;DR: By allowing existential variables in STriPS and treating them properly, CSPs can be represented and solved in a direct manner as action-less, fluent-less STRIPS planning problems, something important for problems involving restrictions.
Proceedings ArticleDOI
Program Debugging Using Constraints -- Is it Feasible?
Franz Wotawa,Mihai Nica +1 more
TL;DR: There is a very good correspondence between the running time required for finding bugs and the structure of the program's constraint representation, and the constraint satisfaction approach is very promising when focusing on debugging methods and functions with an expected debugging time of less than 1 1/2 minute.
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.
Book
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.