Department of Computer Science
University of Toronto
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 uncertainty 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.
Craig Boutilier received his Ph.D. in Computer Science (1992) from the University of Toronto, Canada. He is Professor and Chair of the Department of Computer Science at the University of Toronto. He was previously an Associate Professor at the University of British Columbia, a consulting professor at Stanford University, and has served on the Technical Advisory Board of CombineNet, Inc. since 2001.
Dr. Boutilier's research interests spann a wide range of topics, with a focus on decision making under uncertainty. He has been awarded the Isaac Walton Killam Research Fellowship, and an IBM Faculty Award. He also received the Killam Teaching Award.