School of Industrial and Management Engineering, Korea University, Seoul, Republic of Korea.
PLoS One. 2022 Mar 3;17(3):e0264550. doi: 10.1371/journal.pone.0264550. eCollection 2022.
In real-time strategy games, players collect resources, control various units, and create strategies to win. 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. However, previous studies have mainly focused on predicting game results. 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. We used replay data from StarCraft Ⅱ that is similar to video data providing continuous multiple images. 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. Second, we used gradient-weighted class activation mapping to extract information defining the key situations that significantly influenced the outcomes of the game. 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. We verified the usefulness of our methodology on a 3D-ResNet with a gradient class activation map linked to a StarCraft Ⅱ replay dataset.
在实时战略游戏中，玩家收集资源、控制各种单位并制定策略以取得胜利。制定获胜策略需要准确地分析之前的游戏；因此，能够识别决定游戏结果的关键情况非常重要。然而，以前的研究主要集中在预测游戏结果上。在这项研究中，我们提出了一种方法，以预测星际争霸 II 等最受欢迎的实时战略游戏的结果，并确定决定结果的转折点的信息。我们使用了类似于提供连续多个图像的视频数据的星际争霸 II 重放数据。首先，我们使用 3D 残差网络 (3D-ResNet) 和重放数据训练了一个结果预测模型，以通过利用游戏中的时空信息来提高预测性能。其次，我们使用梯度加权类激活映射来提取定义关键情况的信息，这些关键情况对游戏结果有重大影响。然后，我们通过比较仅使用一个时间点信息的 2D 残差网络 (2D-ResNet) 和具有多个时间点信息的 3D-ResNet 来证明我们的方法具有优越性。我们在一个与星际争霸 II 重放数据集相关联的具有梯度类激活图的 3D-ResNet 上验证了我们方法的有用性。
