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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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Proceedings Article
Abductive Diagnosis of Complex Active Systems with Compiled Knowledge.
TL;DR: An abduction-based diagnosis technique for a class of discrete-event systems (DESs), called complex active systems (CASs), is presented and the correctness (soundness and completeness) of the technique is formally proven.
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Optimising memory management for Belief Propagation in Junction Trees using GPGPUs
TL;DR: A novel approach to parallelise BP with GPGPUs is proposed, which focuses on optimising the memory layout of the BN tables so to achieve better performance in terms of increased speedup, reduced data transfers between the host and the GPG PU, and scalability.
BookDOI
Graph Structures for Knowledge Representation and Reasoning
TL;DR: This volume contains revised selected papers of the third edition of GKR, which took place in Beijing, China on August 3, 2013, and was very successful, with many topics related to graph-based knowledge representation and reasoning.
Proceedings ArticleDOI
Issues and Challenges in Resource Management and its interaction with Levels 2/3 fusion with applications to real-world problems: an Annotated Perspective
Erik Blasch,Ivan Kadar,Kenneth J. Hintz,Joachim Biermann,Chee-Yee Chong,John J. Salerno,Subrata Das +6 more
TL;DR: A panel session was conducted at the International Society of Information Fusion Conference in 2006 to discuss the various issues surrounding the interaction of Resource Management with Level 2/3 Situation and Threat Assessment.
Journal ArticleDOI
Studying the use and effect of graph decomposition in qualitative spatial and temporal reasoning
TL;DR: It is proved that a recently proposed decomposition-based approach for checking the satisfiability of qualitative spatial constraint networks lacks soundness, and a particular tree decomposition is presented that allows for cost-free utilization of parallelism for a qualitative constraint language that has patchwork for satisfiable atomic networks.
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.
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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.