Rank Pruning for Dominance Queries in CP-Nets

TL;DR

This paper introduces a new method for quantifying and ordering user preferences in CP-nets, improving the efficiency of dominance testing and handling indifferences, with demonstrated experimental advantages.

Contribution

A novel approach to quantify preferences in CP-nets that enhances outcome ordering and dominance testing efficiency, including cases with indifference.

Findings

Method outperforms existing techniques in dominance testing efficiency

Effective for CP-nets with indifference between variable values

Experimental results confirm improved preference reasoning

Abstract

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.