  Decision theory does not traditionally include uncertainty over utility
functions. We argue that the a person's utility value for a given outcome can
be treated as we treat other domain attributes: as a random variable with a
density function over its possible values. We show that we can apply
statistical density estimation techniques to learn such a density function from
a database of partially elicited utility functions. In particular, we define a
Bayesian learning framework for this problem, assuming the distribution over
utilities is a mixture of Gaussians, where the mixture components represent
statistically coherent subpopulations. We can also extend our techniques to the
problem of discovering generalized additivity structure in the utility
functions in the population. We define a Bayesian model selection criterion for
utility function structure and a search procedure over structures. The
factorization of the utilities in the learned model, and the generalization
obtained from density estimation, allows us to provide robust estimates of
utilities using a significantly smaller number of utility elicitation
questions. We experiment with our technique on synthetic utility data and on a
real database of utility functions in the domain of prenatal diagnosis.

Artificial Intelligence;Machine Learning