Preference Elicitation in Constraint-Based Decision Problems

Preference elicitation is generally required when making or recommending decisions on behalf of users whose utility function is not known with certainty. Although one can engage in elicitation until a utility function is perfectly known, in practice, this is infeasible. This talk tackles this problem in constraint-based optimization. I will first describe a graphical model for utility representation and issues associated with elicitation in this model. I then discuss two methods for optimization with imprecise utility information: a Bayesian approach in which utility function ncertainty is quantified probabilistically; and a distribution-free minimax regret model. Finally, I will describe several heuristic strategies for elicitation.

This work describes several joint projects with: Darius Braziunas, Relu Patrascu, Pascal Poupart and Dale Schuurmans.