Craig Boutilier
Department of Computer Science
University of Toronto
Toronto, ON M5S 3H5
email: cebly@cs.toronto.edu
Abstract
Intelligent agents often need to assess user utility functions in order to make
decisions on their behalf, or predict their behavior. When uncertainty exists
over the precise nature of this utility function, one can model this
uncertainty using a distribution over utility functions. This view lies at the
core of games with incomplete information and, more recently, several proposals
for incremental preference elicitation. In such cases, decisions (or predicted
behavior) are based on computing the \emph{expected} expected utility (EEU) of
decisions with respect to the distribution over utility functions.
Unfortunately, decisions made under EEU are sensitive to the precise
representation of the utility function. We examine the conditions under which
EEU provides for sensible decisions by appeal to the foundational axioms of
decision theory. We also discuss the impact these conditions have on the
enterprise of preference elicitation more broadly.
To appear, IJCAI-03
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