MATEC Web Conf.
Volume 232, 20182018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
|Number of page(s)||5|
|Section||Network Security System, Neural Network and Data Information|
|Published online||19 November 2018|
A multi-size convolution neural network for RTS games winner prediction
College of System Engineering, National University of Defense Technology, Changsha, China
a Corresponding author: email@example.com
Researches of AI planning in Real-Time Strategy (RTS) games have been widely applied to human behavior modeling and combat simulation. Winner prediction is an important research area for AI planning, which ensures the decision accuracy. In this paper, we introduce an effective architecture -- multi-size convolution neural network (MSCNN)-- into winner prediction. It can capture more feature for game states, because of the various sizes of filters in MSCNN. Experiments show that the modified evaluating algorithm can effectively improve the accuracy of winner prediction for RTS games.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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