Decision-support with preference constraints

One approach to Human Centered Processing is to take into account preferences of users within the context of multiple criteria optimization. The preference model of a problem encloses all the information needed to evaluate the quality of solutions. In this article, we propose a new soft constraint called preference constraint, based on the decision theory concept of binary preference relations. Preference-based constraint systems are defined and a generic algorithm, searching for best quality solutions, is then described. Finally, global constraints, based on a customizable level consistency [G. Verfaillie, D. Martinez, C. Bessiere, A generic customizable framework for inverse local consistency, in: Proc. of the 16th National Conf. on Artificial Intelligence and 11th Conf. on Innovative Applications of Artificial Intelligence, Orlando, Florida], are proposed for solving the preference constraint associated to the Pareto aggregation rule. This new model offers greater flexibility to represent and make complex decisions on computers. (c) 2005 Elsevier B.V. All rights reserved.