Proceedings Abstracts of the Twenty-Third International Joint Conference on Artificial Intelligence

Upper Confidence Weighted Learning for Efficient Exploration in Multiclass Prediction with Binary Feedback / 2488
Hung Ngo, Matthew Luciw, Ngo Anh Vien, Jürgen Schmidhuber

We introduce a novel algorithm called Upper Confidence Weighted Learning (UCWL) for online multiclass learning from binary feedback. UCWL combines the Upper Confidence Bound (UCB) framework with the Soft Confidence Weighted (SCW) online learning scheme.  UCWL achieves state of the art performance (especially on noisy and non-separable data) with low computational costs. Estimated confidence intervals are used for informed exploration, which enables faster learning than the uninformed exploration case or the case where exploration is not used.  The targeted application setting is human-robot interaction (HRI), in which a robot is learning to classify its observations while a human teaches it by providing only binary feedback (e.g., right/wrong).  Results in an HRI experiment, and with two benchmark datasets, show UCWL outperforms other algorithms in the online binary feedback setting, and surprisingly even sometimes beats state-of-the-art algorithms that get full feedback, while UCWL gets only binary feedback on the same data.