Similarly, the number of people employed in e-sports, such as professional gamers, commentators, and coaching staff, continues to increase. Numerous competitions related to these games are held and broadcast.
Real-time strategy games such as StarCraft, StarCraft Ⅱ, and MOBA games such as League of Legends and Dota 2 are leading the growth of the gaming industry. Various genres such as real-time strategy games, first-person shooter games (FPS), role-playing games (RPG), and multiplayer online battle arenas (MOBA) have proven especially popular. The gaming industry has grown to such an extent in popularity that one segment of it has been recognized as a new competition category, e-sports. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. IB was supported by the Brain Korea 21 FOUR, Ministry of Science and ICT in Korea under the ITRC support program (IITP-749), and the Ministry of Culture, Sports and Tourism and Korea Creative Content Agency (R2019020067). įunding: SBK was supported by a National Research Foundation of Korea grant funded by the MSIT (NRF-2019R1A4A1024732). 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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