Utility Elicitation as a Classification Problem
  We investigate the application of classification techniques to utility
elicitation. In a decision problem, two sets of parameters must generally be
elicited: the probabilities and the utilities. While the prior and conditional
probabilities in the model do not change from user to user, the utility models
do. Thus it is necessary to elicit a utility model separately for each new
user. Elicitation is long and tedious, particularly if the outcome space is
large and not decomposable. There are two common approaches to utility function
elicitation. The first is to base the determination of the users utility
function solely ON elicitation OF qualitative preferences.The second makes
assumptions about the form AND decomposability OF the utility function.Here we
take a different approach: we attempt TO identify the new USERs utility
function based on classification relative to a database of previously collected
utility functions. We do this by identifying clusters of utility functions that
minimize an appropriate distance measure. Having identified the clusters, we
develop a classification scheme that requires many fewer and simpler
assessments than full utility elicitation and is more robust than utility
elicitation based solely on preferences. We have tested our algorithm on a
small database of utility functions in a prenatal diagnosis domain and the
results are quite promising.
