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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Journal ArticleDOI
Algorithms for finding maximum transitive subtournaments
TL;DR: Two backtrack algorithms and a Russian doll search algorithm for finding a maximum transitive subtournament in a directed graph are discussed and experimental results of their performance are reported.
Book ChapterDOI
SMT-Constrained Symbolic Execution for Eclipse CDT/Codan
TL;DR: A symbolic execution plug-in extension for Eclipse CDT/Codan serves to reason about satisfiable paths of C programs and can serve as a basis for path-sensitive static bug detection with bounded or unrestricted context.
Search: from Algorithms to Systems
TL;DR: In this paper, the authors propose a taxonomy of search processes w.r.t. their computation characteristics, and provide a rule-based characterization of autonomous solvers, which allows a formalizing of solvers adaptations and modifications with computation rules that describe the modi-fication of the solver's components transformation.
Proceedings Article
Elimination ordering in lifted first-order probabilistic inference
Seyed Mehran Kazemi,David Poole +1 more
TL;DR: It is shown that heuristics proposed to find good orderings in the non-relational models are inefficient for relational models, because they fail to consider the population sizes associated with logical variables.
Proceedings ArticleDOI
Automated synthesis of sustainable data centers
Tom Christian,Yuan Chen,Rocky Shih,Ratnesh Sharma,Christopher Hoover,Manish Marwah,Amip J. Shah,Daniel Gmach +7 more
TL;DR: An Automated Data Center Synthesizer is proposed to design Sustainable Data Centers that meet SLA goals, minimize carbon emissions and embedded exergy, are optimally efficient and deliver significantly reduced Total Cost of Ownership (TCO).
References
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