A Hybrid Approach to Reasoning with Partially Elicited Preference Models
  Classical Decision Theory provides a normative framework for representing and
reasoning about complex preferences. Straightforward application of this theory
to automate decision making is difficult due to high elicitation cost. In
response to this problem, researchers have recently developed a number of
qualitative, logic-oriented approaches for representing and reasoning about
references. While effectively addressing some expressiveness issues, these
logics have not proven powerful enough for building practical automated
decision making systems. In this paper we present a hybrid approach to
preference elicitation and decision making that is grounded in classical
multi-attribute utility theory, but can make effective use of the expressive
power of qualitative approaches. Specifically, assuming a partially specified
multilinear utility function, we show how comparative statements about classes
of decision alternatives can be used to further constrain the utility function
and thus identify sup-optimal alternatives. This work demonstrates that
quantitative and qualitative approaches can be synergistically integrated to
provide effective and flexible decision support.
