Morgan Kaufmann, 19 mai 2003 - 481 pages
This book provides a comprehensive and much needed introduction to the field by one of its foremost experts. It is beautifully written and presents a unifying framework capturing a wide range of techniques for processing symbolic, numerical, and probabilistic information.
--Bart Selman, Cornell University
I've been waiting a long time for a good theoretical introduction to constraint programming. Rina Dechter's book is just this. If you want to understand why this technology works, and how to make it work for you, then I recommend you read this book.
--Toby Walsh, Cork Constraint Computation Centre
The book is rigorous but it is not difficult to read. An abundance of examples illustrate concepts and algorithms. The reader is well guided through technical issues, so intuition is never hidden by technicalities.
--Pedro Meseguer, Institut d'Investigació en Intel.lingència Artificial - Consejo Superior de Investigaciones Científicas (IIIA-CSIC)
An indispensable resource for researchers and practitioners in AI and optimization.
--Henry Kautz, University of Washington
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
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acyclic algebra applied arc-consistency Artiﬁcial Intelligence assignment backjumping backtrack-free backtracking search binary constraints binary network Boolean bound bounds-consistency bucket elimination Chapter chordal graphs clause cliques compute conﬂict set Consider constraint graph constraint language constraint network constraint problem constraint processing constraint propagation constraint satisfaction problems cutset cycle-cutset Dechter deﬁned deﬁnition denoted dual graph efﬁcient elimination algorithm enforcing example exponential Figure ﬁnd ﬁnding ﬁnite domain ﬁrst forward-checking given globally consistent goal heuristic hypergraph inconsistent indicator problem induced graph induced width inference Input instantiation integers intervals join-tree linear logic programming look-ahead maximal mini-bucket minimal network no-good nodes number of variables optimal ordered graph ordering d1 Output pair path-consistency primal graph procedure propositional random relational arc-consistency row convex satisﬁes scope search algorithm search space solved space complexity speciﬁc straint subproblem subset temporal THEOREM tractable languages tree decomposition tuple unit propagation variable elimination variable ordering variable xi yields