Open AccessBook
Constraint Processing
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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Proceedings ArticleDOI
User-Centered QoS in Combining Web Services for Interactive Domain
Arif Bramantoro,Toru Ishida +1 more
TL;DR: This paper proposed a novel twostage approach for combining services in user-centered QoS, i.e. intra- workflow and inter-workflow service selection, which is used to calculate the most optimal QoS value for each composite service.
Proceedings Article
Combining constraint processing and pattern matching to describe and locate structured motifs in genomic sequences.
TL;DR: This paper represents RNA as structured motifs that occur on more than one sequence and which are related together by possible hybridization and offers a simple and extensible framework to solve such problems.
Journal ArticleDOI
An improved DPOP algorithm based on breadth first search pseudo-tree for distributed constraint optimization
Ziyu Chen,Zhen He,Chen He +2 more
TL;DR: This work proposes a new DPOP algorithm using a Breadth First Search (BFS) pseudo-tree as the communication structure, named BFSDPOP, and compares it with original DPOP on three types of problems - graph coloring problems, meeting scheduling problems and random DCOPs.
U-GDL: A decentralised algorithm for DCOPs with Uncertainty
TL;DR: U-DCOPs with uncertainty is introduced, a novel generalisation of the canonical DCOP framework where the outcomes of local functions are represented by random variables, and the global objective is to maximise the expectation of an arbitrary utility function applied over the sum of these local functions.
Journal ArticleDOI
Constraint satisfaction problems and global cardinality constraints
Andrei A. Bulatov,Dániel Marx +1 more
TL;DR: The main result of this paper characterizes sets Γ that give rise to problems solvable in polynomial time, and states that the remaining such problems are NP-complete.
References
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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.