Tactical Maneuver Strategy Learning from Land Wargame Replay Based on Convolutional Neural Network

Authors

Jiale Xu, 1.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China;2.Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;Follow
Haidong Zhang, 2.Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;3.Innovation Academy for Artificial Intelligence, Chinese Acdemy of Sciences, Beijing 100190, China;
Donghai Zhao, 4.Joint Operations College, National Defense University, Shijiazhuang 050000, China;
Wancheng Ni, 2.Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;3.Innovation Academy for Artificial Intelligence, Chinese Acdemy of Sciences, Beijing 100190, China;Follow

Abstract

Abstract: Aiming at collecting the high valuable knowledge of action decisions in "man-in-the-loop" wargame's replay data, a method of using convolutional neural network to learn the tactical maneuver strategy model from the replay data of wargame is proposed. In this method, the tactical maneuver strategy is modeled as a classification problem of making a good choice from the target candidate locations under the influence of current situation. The key factors affecting commander's decision-making are summarized, and the basic situation features are defined, which are composed of seven attributes such as "maneuverability range and observation range". The feature dataset with positive and negative labels is established. The classifier based on convolutional neural network is designed, which can predict the maneuver terminal position of a single piece by the classification probability. Experimental results show that the prediction accuracy of the tactical maneuver strategy model based on the convolutional neural network is up to 78.96%, which is improved by at least 4.59% compared with other models.

Recommended Citation

Xu, Jiale; Zhang, Haidong; Zhao, Donghai; and Ni, Wancheng (2022) "Tactical Maneuver Strategy Learning from Land Wargame Replay Based on Convolutional Neural Network," Journal of System Simulation: Vol. 34: Iss. 10, Article 8.
DOI: 10.16182/j.issn1004731x.joss.21-0429
Available at: https://dc-china-simulation.researchcommons.org/journal/vol34/iss10/8

First Page

2181

Revised Date

2021-07-20

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-0429

Last Page

2193

CLC

TP391.9

Recommended Citation

Jiale Xu, Haidong Zhang, Donghai Zhao, Wancheng Ni. Tactical Maneuver Strategy Learning from Land Wargame Replay Based on Convolutional Neural Network[J]. Journal of System Simulation, 2022, 34(10): 2181-2193.

Corresponding Author

Wancheng Ni,wancheng.ni@ia.ac.cn

DOI

10.16182/j.issn1004731x.joss.21-0429