Tianhan Wang
Department of Computer Science
University of Toronto
Toronto, ON M5S 3H5
email: tianhan@cs.toronto.edu
Craig Boutilier
Department of Computer Science
University of Toronto
Toronto, ON M5S 3H5
email: cebly@cs.toronto.edu
Abstract
Utility elicitation is a critical function of any automated decision aid,
allowing decisions to be tailored to the preferences of a specific user.
However, the size and complexity of utility functions often precludes full
elicitation, requiring that decisions be made without full utility information.
Adopting the minimax regret criterion for decision making with
incomplete utility information, we describe and empirically compare several new
procedures for incremental elicitation of utility functions that attempt to
reduce minimax regret with as few questions as possible. Specifically, using
the (continuous) space of standard gamble queries, we show that myopically
optimal queries can be computed effectively (in polynomial time) for several
different improvement criteria. One such criterion, in particular, empirically
outperforms the others we examine considerably, and has provable improvement
guarantees.
To appear, IJCAI-03
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