Edward Tsang, Chang Wang, Jim Doran, James Borrett, Andrew Davenport, Kangmin Zhu, Chris Voudouris Constraint satisfaction is a core of many problems tackled in AI, such as scheduling and temporal reasoning. The importance of constraint satisfaction has led to the development of commercially available constraint satisfaction programming languages and systems, such as CHIP, ECLiPSe and ILOG Solver. However, these are too slow for applications in which rapid solutions are critical or vital, for instance, in interactive systems and problems in which one would like to examine many variables and constraints combinations. Moreover, many real world problems are so tightly constrained that not all the constraints are satisfiable. In such problems near optimal solutions are called for.
Complete algorithms are unlikely to be suitable for such problems.
Connectionist approaches have shown good potential in meeting the requirements of the above applications.