A Confidence-Calibrated MOBA Game Winner Predictor
Abstract
In this paper, we propose a confidence-calibration method for predicting the winner of a famous multiplayer online battle arena (MOBA) game, League of Legends. In MOBA games, the dataset may contain a large amount of input-dependent noise; not all of such noise is observable. Hence, it is desirable to attempt a confidence-calibrated prediction. Unfortunately, most existing confidence calibration methods are pertaining to image and document classification tasks where consideration on uncertainty is not crucial. In this paper, we propose a novel calibration method that takes data uncertainty into consideration. The proposed method achieves an outstanding expected calibration error (ECE) (0.57%) mainly owing to data uncertainty consideration, compared to a conventional temperature scaling method of which ECE value is 1.11%.
Keywords
Cite
@article{arxiv.2006.15521,
title = {A Confidence-Calibrated MOBA Game Winner Predictor},
author = {Dong-Hee Kim and Changwoo Lee and Ki-Seok Chung},
journal= {arXiv preprint arXiv:2006.15521},
year = {2020}
}
Comments
Submitted to IEEE Conference on Games(CoG) 2020