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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
Tradeoffs in the complexity of backdoors to satisfiability: dynamic sub-solvers and learning during search
TL;DR: The notion of backdoors is extended to incorporate learning during search—a key aspect of nearly all state-of-the-art systematic SAT solvers—and it is shown that this drastically reduces the size of the resulting backdoor set.
Book ChapterDOI
A secure Workflow model based on distributed constrained role and task assignment for the internet
TL;DR: A new Workflow Management System (WFMS) model is presented, that uses a Trust Establishment framework that enables creating dynamic user-role assignment where not all users are known in advance, and can fit into dynamic environments where new users are added, or credentials of existing users are revoked.
Book ChapterDOI
Applying constraint programming to identification and assignment of service professionals
Sigal Asaf,Haggai Eran,Yossi Richter,Daniel Patrick Connors,Donna L. Gresh,Julio Ortega,Michael J. Mcinnis +6 more
TL;DR: This work focuses on providing a Constraint Programming solution for supporting the assignment of highly-skilled professionals and has built a tool that is currently being used by IBM service organizations and provides strong business results.
Dissertation
Adaptive Time- and Process-Aware Information Systems
TL;DR: The ATAPIS framework provides fundamental concepts, techniques and algorithms for integrating the time perspective of business processes in PAISs and ensures that a time-aware process instance may be executed without violating any of its temporal constraints.
Journal ArticleDOI
Handling fuzzy temporal constraints in a planning environment
Marc de la Asunción,Luis Castillo,Juan Fernández-Olivares,Óscar García-Pérez,Antonio González,Francisco Palao +5 more
TL;DR: The resulting approach is able to tackle problems with ill defined knowledge, to obtain plans that are approximately consistent and to adapt the execution of plans to unexpected delays.
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.
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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.