Open AccessBook
Constraint Processing
Reads0
Chats0
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
More filters
Journal ArticleDOI
Reasoning With Topological And Directional Spatial Information
Sanjiang Li,Anthony G. Cohn +1 more
TL;DR: In this paper, a bipath-consistency algorithm BipathConsistency is shown to be incomplete for solving even basic RCC8 and RA constraints, and a method to compute solutions that satisfy all topological constraints and approximately satisfy each RA constraint to any prescribed precision is given.
Stable Coalition formation among energy consumers in the Smart Grid
TL;DR: This paper proposes the concept of virtual energy consumer (VEC) to capture the notion of a number of energy consumers, coming together to buy electricity, as an aggregate and proposes a networkrestricted coalitional game to create such VEC’s.
Journal Article
Efficient strategies for (weighted) maximum satisfiability
Zhao Xing,Weixiong Zhang +1 more
TL;DR: A number of strategies to significantly improve this max-SAT method are proposed, including a set of unit propagation rules; an effective lookahead heuristic based on linear programming; and a dynamic variable ordering that exploits problem constrainedness during search.
Proceedings ArticleDOI
Optimal temporal decoupling in multiagent systems
TL;DR: This paper focuses on a constraint problem that is efficiently solvable, but still very relevant and interesting in the context of multiple agents executing their actions, i.e. the Simple Temporal Problem (STP), and shows that finding an optimal decoupling is at least as hard as finding a solution for the constraint problem.
Book ChapterDOI
YIELDS: A Yet Improved Limited Discrepancy Search for CSPs
TL;DR: The learning scheme, which is the main contribution of this paper, takes benefit from failures encountered during search in order to enhance the efficiency of variable ordering heuristic and obtains a search which needs less discrepancies than LDS to find a solution or to state a problem is intractable.
References
More filters
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.
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.