Achim Rettinger, Martin Zinkevich, Michael Bowling
Many learning tasks in adversarial domains tend to be highly dependent on the opponent. Predefined strategies optimized for play against a specific opponent are not likely to succeed when employed against another opponent. Learning a strategy for each new opponent from scratch, though, is inefficient as one is likely to encounter the same or similar opponents again. We call this particular variant of inductive transfer a concept recall problem. We present an extension to AdaBoost called ExpBoost that is especially designed for such a sequential learning tasks. It automatically balances between an ensemble of experts each trained on one known opponent and learning the concept of the new opponent. We present and compare results of ExpBoost and other algorithms on both synthetic data and in a simulated robot soccer task. ExpBoost can rapidly adjust to new concepts and achieve performance comparable to a classifier trained exclusively on a particular opponent with far more data.
Subjects: 12. Machine Learning and Discovery