Regularized Least-Squares for Learning Non-Transitive Preferences between Strategies
: Pahikkala T, Tsivtsivadze E, Airola A, Salakoski T
: Raiko T, Haikonen P, Väyrynen J
: 13th Finnish Artificial Intelligence Conference
Publisher: Finnish Artificial Intelligence Society
: 2008
: Publications of the Finnish Artificial Intelligence Society
: Proceedings of the 13th Finnish Artificial Intelligence Conference and Nokia Workshop on Machine Consciousness
: Publications of the Finnish Artificial Intelligence Society
: 24
: 978-952-5677-04-1
: 978-952-5677-05-8
: 1238-4658
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.
