It is a challenge for existing artificial intelligence algorithms to deal with incomplete information of computer tactical wargames in military research, and one effective method is to take advantage of game replays based on data mining or supervised learning. However, the open source datasets of wargame replays are extremely rare, which obstruct the development of research on computer wargames. In this paper, a data set of wargame replays is opened for predicting algorithm on the condition of incomplete information, to be specific, we propose the dataset processing method for deep learning and an network model for enemy locations predicting. We first introduce the criteria and methods of data preprocessing, parsing and feature extraction, then the training set and test set for deep learning are predefined. Furthermore, we have designed a newly specific network model for enemy locations predicting, including multi-head input, multi-head output, CNN and GRU layers to deal with the multi-agent and long-term memory problems. The experimental results demonstrate that our method achieves good performance of 84.9% on top-50 accuracy. Finally, we open source the data set and methods on https://github.com/daman043/AAGWS-Wargame-master.

中文翻译：

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引入基于深度学习的战争游戏中位置预测的新数据集和方法

现有的人工智能算法在军事研究中处理计算机战术战争游戏的不完整信息是一个挑战，一种有效的方法是利用基于数据挖掘或监督学习的游戏重播。但是，战争游戏回放的开源数据集极为罕见，这阻碍了计算机战争游戏研究的发展。本文针对信息不完全的情况，开放了战争游戏回放数据集用于预测算法，具体而言，提出了深度学习的数据集处理方法和敌人位置预测的网络模型。我们首先介绍数据预处理，解析和特征提取的标准和方法，然后预先定义用于深度学习的训练集和测试集。此外，我们为敌人的位置预测设计了一个新的网络模型，包括多头输入，多头输出，CNN和GRU层，以应对多主体和长期记忆问题。实验结果表明，我们的方法在前50个精度上均达到了84.9％的良好性能。最后，我们在https://github.com/daman043/AAGWS-Wargame-master上开源数据集和方法。