A4 Refereed article in a conference publication
Regularized Least-Squares for Learning Non-Transitive Preferences between Strategies
Authors: Pahikkala T, Tsivtsivadze E, Airola A, Salakoski T
Editors: Raiko T, Haikonen P, Väyrynen J
Conference name: 13th Finnish Artificial Intelligence Conference
Publisher: Finnish Artificial Intelligence Society
Publication year: 2008
Journal: Publications of the Finnish Artificial Intelligence Society
Book title : Proceedings of the 13th Finnish Artificial Intelligence Conference and Nokia Workshop on Machine Consciousness
Series title: Publications of the Finnish Artificial Intelligence Society
Volume: 24
ISBN: 978-952-5677-04-1
eISBN: 978-952-5677-05-8
ISSN: 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.
Downloadable publication This is an electronic reprint of the original article. |  |
