Decision making systems require methods for concisely representing and
efficiently reasoning with human preferences. This talk will cover
three stages of work achieving this goal. First we discuss ceteris
paribus (all-else equal) preference statements as one such
representation of natural and intuitive human preferences over outcomes
or goals. Although deduction in a logic of such preference statements
can compare the desirability of specific conditions or goals, many
decision-making methods require numerical measures of degrees of
desirability. Second, to permit ceteris paribus specifications of
preferences while providing quantitative comparisons, we present an
algorithm that compiles a set of qualitative ceteris paribus preferences
into an ordinal utility function. Constructing the utility function
can, in the worst case, take time exponential in the number of features.
Finally, we introduce common utility-independence conditions that reduce
the computational burden of constructing and evaluating the presented
utility function. Our heuristics use utility independence coupled with
constraint-based search to obtain efficient utility functions. As a
whole, our methods form a reasoning system that goes from abstract,
logical preferences to decision making by using concrete utility
functions. Our system is always correct and, under common conditions,
computationally efficient.