  Conditional preference networks (CP-nets) are a graphical representation of a
person's (conditional) preferences over a set of discrete variables. In this
paper, we introduce a novel method of quantifying preference for any given
outcome based on a CP-net representation of a user's preferences. We
demonstrate that these values are useful for reasoning about user preferences.
In particular, they allow us to order (any subset of) the possible outcomes in
accordance with the user's preferences. Further, these values can be used to
improve the efficiency of outcome dominance testing. That is, given a pair of
outcomes, we can determine which the user prefers more efficiently. Through
experimental results, we show that this method is more effective than existing
techniques for improving dominance testing efficiency. We show that the above
results also hold for CP-nets that express indifference between variable
values.
