Introduction of a new dataset and method for location predicting based on deep learning in wargame

Liu M, Zhang H, Hao W, Qi X, Cheng K, Jin D, Feng X (2021)


Publication Type: Journal article

Publication year: 2021

Journal

Book Volume: 40

Pages Range: 9259-9275

Journal Issue: 5

DOI: 10.3233/JIFS-201726

Abstract

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.

Involved external institutions

How to cite

APA:

Liu, M., Zhang, H., Hao, W., Qi, X., Cheng, K., Jin, D., & Feng, X. (2021). Introduction of a new dataset and method for location predicting based on deep learning in wargame. Journal of Intelligent & Fuzzy Systems, 40(5), 9259-9275. https://dx.doi.org/10.3233/JIFS-201726

MLA:

Liu, Man, et al. "Introduction of a new dataset and method for location predicting based on deep learning in wargame." Journal of Intelligent & Fuzzy Systems 40.5 (2021): 9259-9275.

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