Craig Boutilier
Department of Computer Science
University of Toronto
Toronto, ON M5S 3H5
email: cebly@cs.toronto.edu
Abstract
Preference elicitation is a key problem facing the deployment of
intelligent systems that make or recommend decisions on the behalf of
users.
Since not all aspects of a utility function have the same impact on
object-level decision quality, determining which information to extract from a
user is itself
a sequential decision problem, balancing the amount of elicitation
effort and time with decision quality. We formulate this problem as
a partially-observable Markov decision process (POMDP). Because of the
continuous nature of the state and action spaces of this POMDP,
standard techniques cannot be used to solve it. We describe
methods that exploit the special structure of preference
elicitation to deal with parameterized belief states over the continuous
state space, and gradient techniques for optimizing parameterized actions.
These methods can be used with a number of different belief state
representations, including mixture models.
To appear, AAAI-02
Return to List of Papers