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Constraint Processing
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TLDR
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
An effective algorithm for and phase transitions of the directed hamiltonian cycle problem
Gerold Jäger,Weixiong Zhang +1 more
TL;DR: In this article, an algorithm based on Boolean satisfiability (SAT) was proposed for the Hamiltonian cycle problem (HCP) in directed graphs, which significantly outperforms previous exact HCP algorithms.
Proceedings ArticleDOI
Resource management and its interaction with level 2/3 fusion from the fusion06 panel discussion
TL;DR: This paper summarizes the key tenants of the discussion to filter vast experiences of the invited panel experts on sensor resource management and addressing constraints for resource planning and scheduling.
Journal ArticleDOI
MiniBrass: Soft constraints for MiniZinc
TL;DR: MiniBrass is introduced, a versatile soft constraint modeling language building on the unifying algebraic framework of partially ordered valuation structures (PVS) that is implemented as an extension of MiniZinc and MiniSearch and provides the most general construction of a c-semiring from an arbitrary PVS.
Proceedings ArticleDOI
Integrating loop and data optimizations for locality within a constraint network based framework
TL;DR: This paper presents a constraint network (CN) based formulation of the integrated loop-data optimization problem, and presents two alternate solutions to the data locality problem with the authors' CN based formulation and discusses the pros and cons of each scheme.
Dissertation
Managing complex scheduling problems with dynamic and hybrid constraints
TL;DR: This dissertation introduces a new representation called the Dynamic Disjunctive Temporal Problem, along with several techniques to improve both efficiency and stability when solving one, and considers scheduling problems in which a mixture of finite-domain and temporal variables can interact through hybrid constraints.
References
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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.