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. Bessière, 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.