UTCS Colloquium/AI: Craig Boutilier/University of Toronto Regret-based Methods for Preference Elicitation and Mechanism Design ACES 2.302 Friday September 28 2007 11:00 a.m.

Contact Name: 
Jenna Whitney
Sep 28, 2007 11:00am - 12:00pm

There is a sign-up schedule for this event:

Type of Talk: UTCS Colloquium/AI

Speaker Name/Affiliation: C

raig Boutilier/University of Toronto

Date: Friday September 28 20

07 at 11:00 a.m.

Location: ACES 2.302

Host: Peter Stone

Talk Title: Regret-based Methods for Preference Elicitation
and Me

chanism Design

Talk Abstract:
Preference elicitation is generally
required when making
or recommending decisions on behalf of users whos

utility function is not known with certainty. Although one
can en

gage in elicitation until a utility function is perfectly
known in pra

ctice this is infeasible. In this talk I explore
the use of minimax r

egret as (a) a distribution-free means
for making decisions with imprec

ise utility information; and
(b) a means for guiding elicitation in a

way that focuses
only on relevant aspects of a user''s preferences. The
will develop efficient integer programming approaches to
problem and heuristic techniques for elicitation.

Preference elicit

ation is of course an important component
of (economic) mechanism des

ign as well. Classical approaches
to mechanism design require participa

nts to fully reveal their
utility functions. Time permitting I will sk

etch some recent
results on the use of minimax regret in the automated

of partial revelation mechanisms. With only partial revelation o

preferences we provide bounds on both incentive and outcome

ity by generalizing VCG.

(This talk describes joint work with variou

s collaborators.)

Speaker Bio:
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 th

e University of British Columbia a consulting
professor at Stanford Un

iversity and has served on the
Technical Advisory Board of CombineNet
Inc. since 2001.

Dr. Boutilier''s research interests span a wide ra

nge of
topics with a focus on decision making under uncertainty

ncluding preference elicitation mechanism design game
theory Markov

decision processes and reinforcement
learning. He is a Fellow of the A

merican Association of
Artificial Intelligence (AAAI) and the recipient
of the Isaac
Walton Killam Research Fellowship an IBM Faculty Award <

br>and the Killam Teaching Award.