Regularized Least-Squares for Learning Non-Transitive Preferences between Strategies

Abstract

<p>Most of the current research in preference learning has concentrated on learning transitive relations. However, there are many interesting problems that are non-transitive. Such a learning task is, for example, the prediction of the probable winner given the strategies of two competitors. In this paper, we investigate whether there is a need to learn non-transitive preferences, and whether they can be learned efficiently. In particular, we consider cyclic preferences such as those observed in the game of rock paper and scissors.<br /></p>