Constraint satisfaction is a decision problem that involves finite choices. It is ubiquitous.
The goal is to find values for a set of variables that will satisfy a
given set of constraints. It is the core of many applications
in artificial intelligence, and has found its application in
many areas, such as planning and scheduling. Because of its generality,
most AI researchers should be able to benefit from having
good knowledge of techniques in this field.
Published in 1993, this book was the first attempt to define the scope of constraint satisfaction. It covers both the theoretical and the implementation aspects of the subject. It provides a framework for studying this field, relates different research, and resolves ambiguity in a number of concepts and algorithms in the literature.
This book is arguably the most rigorous book in the field.
All major concepts were defined in First Order Predicate Calculus (FOPC).
Concepts defined this way are precise and unambiguous.
This book was published by Academic Press:
It is now out of print.
Reprint (with minor corrections*) is available from 2014.05.13:
Minor editorial errors in the 1993 version have been corrected.