  Structured utility models are essential for the effective representation and
elicitation of complex multiattribute utility functions. Generalized additive
independence (GAI) models provide an attractive structural model of user
preferences, offering a balanced tradeoff between simplicity and applicability.
While representation and inference with such models is reasonably well
understood, elicitation of the parameters of such models has been studied less
from a practical perspective. We propose a procedure to elicit GAI model
parameters using only "local" utility queries rather than "global" queries over
full outcomes. Our local queries take full advantage of GAI structure and
provide a sound framework for extending the elicitation procedure to settings
where the uncertainty over utility parameters is represented probabilistically.
We describe experiments using a myopic value-of-information approach to
elicitation in a large GAI model.
