7 of 8 people found the following review helpful:
5.0 out of 5 stars
Chapeau bas!, April 2, 2004
This review is from: Constraint Processing (The Morgan Kaufmann Series in Artificial Intelligence) (Hardcover)
This book covers both introductory and advanced material. It is a
solid resource for introducing students to the basic mechanisms of
Constraint Processing and for training the future generation of
researchers in the field. It is also an invaluable tool for
perfecting the knowledge of the seasoned researcher in topics beyond
his/her particular expertise.
Its coverage of the relationship between relational algebra and
Constraint Satisfaction is unique and bound to inspire new synergies
between Databases and Constraint Processing. The depth and rigor
at which advanced topics are addressed (e.g., advanced consistency
methods, tree decomposition techniques, and temporal reasoning
networks) are a remarkable achievement, possible only given the wealth
and significance of the author's own contributions to the field.
This is one of those rare books you will enjoy reading over and over
again, every time acquiring new knowledge.
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2 of 2 people found the following review helpful:
4.0 out of 5 stars
a good introduction to constraint processing, May 18, 2010
This review is from: Constraint Processing (The Morgan Kaufmann Series in Artificial Intelligence) (Hardcover)
I consider this book to be a very good place to begin one's study of constraint processing and programming.
Chapters 1-7 cover the essential ideas and issues behind constraint problems. Regardless of what approach
one favors towards solving such problems, the definitions and algorithms provided in these chapters provide
a basis for understanding the different approaches. For the most part,
I found the book well written, and it provided many good examples that helped clarify a definition and/or algorithm.
However, I do have a few criticisms. The main criticism is that the book seems biased towards problems (and hence
algorithms for solving them) whose constraints can be enumerated as a set of tuples over sets having
small finite domains (if the domains are not small, the algorithms become impractical due to time and space complexity).
Such constraints are in fact a small subset of the types of constraints that one may encounter in practice. In practice,
domains sets might be very large (or even infinite!) and people (i.e. scientists, engineers, and business people) tend to
express constraints by using formulas that may not translate so readily into sets of tuples. Thus, if the intent is to use
the information in this book as the basis for establishing a constraint-programming system, then one will have to place
limitations on both the kinds of solvable problems, and how the constraints of the problem can be expressed.
The other main criticism is that there is very little discussion or exposure to stochastic and statistical methods, aside from
the brief introduction to stochastic local search in Chapter 7. I suppose one could have written a similar introductory book in which Monte Carlo methods and other stochastic techniques had center stage. In my opinion, such techniques are necessary to help alleviate the first criticism.
Finally, I would have appreciated a bit more variety in terms of the examples provided. The author received much mileage out of
a few graph-coloring examples, but for some exercises at the end of the chapter, these referred-to examples did not have enough
complexity to illustrate the lesson of the exercise.
However, despite these criticisms, I still give the book four stars, because it is generally well-written with good foundational information, and, to my knowledge, there currently is not a better book on the market.
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2 of 2 people found the following review helpful:
5.0 out of 5 stars
The best reference on Constraints Processing, September 11, 2007
This review is from: Constraint Processing (The Morgan Kaufmann Series in Artificial Intelligence) (Hardcover)
This is the most comprehensive book in the area of Constraint Processing (aka Constraint Solving and CSP) I have seen. It starts with the basics and takes the reader all the way to advanced topics. It is an excellent place to start if you want to learn the field. It is also an excellent reference for researchers and practitioners. I use this book frequently in my research and my work in the field of Formal Verification. The book is also of great value to those in Artificial Intelligence, Optimization, and Operation Research.
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