Learning General CP-nets Using Simulated Annealing

Abstract

Preferences are a primary manner in which decisions are made and there are many methods of representing preference relations over combinatorial domains, we find that conditional preference networks (CP-nets) provide an interesting combination of expressive power, simplicity, and explainability. While these properties make it useful for representing agent preferences it is limited by a lack of efficient and exact learning algorithms. We propose and show that the use of simulated annealing provides a relatively efficient and accurate method of learning CP-nets from a set of pairwise preference examples. Moreover, we show that the CP-nets learned using this method generalize well to unseen examples and outperform baseline trivial and lexicographic models. Additionally, we show that analysis of the pairwise preference example set can reliably indicate whether or not our approach is particularly well-suited to learning the generating preference, with only minimal computation.