Principles of Constraint Programming and Constraint Processing: A Review

Texts in constraint programming are a little like buses. You wait forever for one to come along, and then two come along at once. In this case, there has been a large gap in the market for a theoretical introduction to constraint programming ever since Edward Tsang’s Foundations of Constraint Satisfaction (1993) went out of print.1 Therefore, we are very pleased to see two books written by two of the leading researchers in this field come along to fill the gap. Constraint programming is a very active research area within AI. It is a highly successful technology for solving a wide range of combinatorial problems, including scheduling, rostering, assignment, routing, and design. A number of companies, like ILOG, Dash Optimization, and Parc Technologies, market model building and constraint programming toolkits, which are used by companies as diverse as Amazon.com, British Airways, Chevron, Cisco, Dupont, Ford, General Mills, HP, I2, JD Edwards, KLM, Lockheed Martin, Mannersmann, Nestle, Oracle, Proctor & Gamble, Qwest, Renault, SNCF, UPS, and Volvo. Constraint programming is a declarative style of modeling combinatorial problems in which the user identifies the decision variables, their possible domain of values, and specifies constraints over the allowed values (for example, no two of these variables can take the same value). ■ Apt, Krzysztof. Principles of Constraint Programming. Cambridge, England: Cambridge University Press. ISBN: 0521-825830. 420 pages, $50.00. Publication Date: August 2003. http://uk.cambridge.org