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; Bibliography
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
graduate students and senior undergraduate students in artificial intelligence and operations research; researchers and practitioners in artificial intelligence and operations research
- No. of pages:
- © Morgan Kaufmann 2003
- 5th May 2003
- Morgan Kaufmann
- eBook ISBN:
- Hardcover ISBN:
“To summarize, this book distills well over three decades worth of development in CSP and constraint processing in a single textbook. I wholeheartedly recommend it to students, researchers and practitioners in artificial intelligence, constraint programming and operations research who want to know more about the theory of constraint processing.” — Roland H.C. Yap, National University of Singapore, in Theory and Practice of Logic Programming “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 Intellingència Artificial – Consejo Superior de Investigaciones Científicas (IIIA-CSIC) “An indispensable resource for researchers and practitioners in AI and optimization.” — Henry Kautz, University of Washington “a welcome introduction to the field of constraint satisfaction that will help researchers, educators, and students understand what constraint processing is about. It is a comprehensive book that can be used as a companion for courses on constra
Rina Dechter is a professor of Computer Science at the University of California, Irvine. She received her PhD in Computer Science at UCLA in 1985, a MS degree in Applied Mathematic from the Weizman Institute and a B.S in Mathematics and Ststistics from the Hebrew University, Jerusalem. Her research centers on computational aspects of automated reasoning and knowledge representation including search, constraint processing and probabilistic reasoning. Professor Dechter has authored over 50 research papers, and has served on the editorial boards of: Artificial Intelligence, the Constraint Journal, Journal of Artificial Intelligence Research and the Encyclopedia of AI. She was awarded the Presidential Young investigator award in 1991 and is a fellow of the American association of Artificial Intelligence.
University of California, Irvine