Publication Date
4-2005
Abstract
In traditional constraint satisfaction, constraints are ``hard'' in the sense that we need to satisfy them all. In many practical situations, however, constraints are "soft" in the sense that if we are unable to satisfy some of them, the corresponding solution is still practically useful. In such situations, it is desirable to satisfy as many high-priority constraints as possible. In this paper, we describe an optimal algorithm for solving the corresponding soft constraint problem.
tr04-32a.pdf (105 kB)
Original file: UTEP-CS-04-32a
Original file: UTEP-CS-04-32a
Comments
UTEP-CS-04-32b.
Published in the Proceedings of the 17th World Congress of the International Association for Mathematics and Computers in Simulation IMACS'2005, Paris, France, July 11-15, 2005.