[Intelligence Seminar] TODAY: Craig Boutilier, Wean 5409, 3:30 - "Intelligent preference assessment: the next steps?"
Noah A Smith
nasmith at cs.cmu.edu
Tue Feb 3 13:12:47 EST 2009
February 3, 2009
For meetings, contact Marilyn Walgora (mwalgora at cs.cmu.edu).
Intelligent Preference Assessment: The Next Steps?
Craig Boutilier, 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. Full elicitation of user utility functions is infeasible
in practice, leading to an emphasis on approaches that (a) attempt to
make good recommendations with incomplete utility information; and (b)
heuristically minimize the amount of user interaction needed to assess
relevant aspects of a utility function. Current techniques are,
however, limited in a number of ways: (i) they rely on specific forms
for assessment; (ii) they require very stylized forms of interaction;
(iii) they are limited in the types of decision problems that can
In this talk, I will outline several key research challenges in taking
preference assessment to a point where wide user acceptance is
possible. I will focus on three three current techniques we're
developing that will help move in the direction of greater user
acceptance. Each tackles one of the weaknesses discussed above.
1. The first two techniques allows users to define "personalized"
features over which they can express their preferences. Users provide
(positive and negative) instances of a concept (or feature) over which
they have preferences. We relate this to models of concept learning,
and discuss how they existence of utility functions allows decisions
to be made with very incomplete knowledge of the target concept. I'll
possible means integrating data-intensive collaborative filtering
approaches with explicit preference elicitation techniques, especially
when tackling "subjective" features.
2. I'll discuss some of our recent work on applying explicit
decision-theoretic models to more "conversational" critiquing
approaches to recommender systems. We consider several semantics
(wrt user preferences) for unstructured user choices and show
how these can be integrated into regret-based models.
3. Time permitting, I'll provide a sketch of some recent work
on eliciting reward functions in Markov decision processes using
the notion of minimax regret.
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 a visiting professor
at Brown University. He has served on the Technical Advisory Board of
CombineNet, Inc. since 2001.
Dr. Boutilier's research interests span a wide range of topics, with a
focus on decision making under uncertainty, including preference elicitation,
mechanism design, game theory, Markov decision processes, and reinforcement
learning. He is a Fellow of the American Association of Artificial
Intelligence (AAAI) and the recipient of the Isaac Walton Killam Research
Fellowship, an IBM Faculty Award and the Killam Teaching Award. He has
also served in a variety of conference organization and editorial
positions, and is Program Chair of the upcoming Twenty-first
International Joint Conference on Artificial Intelligence (IJCAI-09).
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