Rome, Italy
Principles of Knowledge Representation and Reasoning
submission
submission
Jan 11-13
opens
ready
opens
Preference Elicitation and Preference Learning in Social Choice: New Foundations for Group Recommendation
Craig BoutillierUniversity of Toronto
download the slides - 
watch the video
Social choice has been the subject of intense investigation within computer science, AI, and operations research, in part because of the ease with which preference data from user populations can now be elicited, assessed, or estimated in online settings. In many domains, the preferences of a group of individuals must be aggregated to form a single consensus recommendation, placing us squarely in the realm of social choice.
The application of social choice and voting schemes to domains like web search, product recommendation and social networks places new emphasis on issues such as: articulating suitable decision criteria; approximation; incremental preference elicitation; learning methods for population preferences; and more nuanced analysis of manipulation.
In this talk, I'll provide an overview of some of these challenges and outline some of our recent work tackling of them, including: learning probabilistic models of population preferences from choice data; robust optimization (winner determination) with incomplete user preferences; incremental preference elicitation for group decision making; and new analyses of manipulation. Each of these poses interesting modeling, knowledge representation and optimization challenges that are best tackled using a combination of techniques from AI, operations research, and statistics.
Brief author biography
Craig Boutilier is a Professor of Computer Science at the University of Toronto. He received his Ph.D from Toronto in 1992, and joined the faculty of University of British Columbia in 1991 (where he remains an Adjunct Professor). He returned to Toronto in 1999, and served as Chair of the Department of Computer Science from 2004-2010. Boutilier has held visiting positions and Stanford, Brown, Carnegie Mellon and Paris-Dauphine, and served on the Technical Advisory Board of CombineNet for nine years.
Boutilier's has published over 180 refereed articles covering topics ranging from knowledge representation, belief revision, default reasoning, and philosophical logic, to probabilistic reasoning, decision making under uncertainty, multiagent systems, and machine learning. His current research efforts focus on various aspects of decision making under uncertainty: preference elicitation, mechanism design, game theory and multiagent decision processes, economic models, social choice, computational advertising, Markov decision processes and reinforcement learning. Boutilier served as Program Chair for both UAI-2000 and IJCAI-09, and is currently Associate Editor-in-Chief of the Journal of Artificial Intelligence Research (JAIR). He is also a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI).