This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The dataset used in this study is available from the following website. Received: AugAccepted: FebruPublished: March 3, 2022Ĭopyright: © 2022 Baek, Kim. PLoS ONE 17(3):Įditor: Jean-Christophe Nebel, Kingston University, UNITED KINGDOM We verified the usefulness of our methodology on a 3D-ResNet with a gradient class activation map linked to a StarCraft Ⅱ replay dataset.Ĭitation: Baek I, Kim SB (2022) 3-Dimensional convolutional neural networks for predicting StarCraft Ⅱ results and extracting key game situations. We then proved that the proposed method outperforms by comparing 2D-residual networks (2D-ResNet) using only one time-point information and 3D-ResNet with multiple time-point information. Second, we used gradient-weighted class activation mapping to extract information defining the key situations that significantly influenced the outcomes of the game. First, we trained a result prediction model using 3D-residual networks (3D-ResNet) and replay data to improve prediction performance by utilizing in-game spatiotemporal information.
We used replay data from StarCraft Ⅱ that is similar to video data providing continuous multiple images. In this study, we propose a methodology to predict outcomes and to identify information about the turning points that determine outcomes in StarCraft Ⅱ, one of the most popular real-time strategy games. However, previous studies have mainly focused on predicting game results. The creation of winning strategies requires accurately analyzing previous games therefore, it is important to be able to identify the key situations that determined the outcomes of those games. In real-time strategy games, players collect resources, control various units, and create strategies to win.
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