Mining of Weapon Utility Based on the Replay Data of War-Game
XING Si-Yuan 1; 2; 3 NI Wan-Cheng 2; 3 ZHANG Hai-Dong 2; 3 YAN Ke 4
1． University of Chinese Academy of Sciences, Beijing 100049, China 2. Innovation Academy for Artificial Intelligence, Chinese Academy of Sciences,
Beijing 100190, China 3．Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 4. School of Joint Operation, Natioal Defense University, Beijing 100091, China
Abstract：In order to acquire commander’s decision-making experience from the “Man in loop” war-game replay data, a correlation analysis model of the fire striking elements is proposed to mine the rules of weapons usage from the replay of Army contract tactical war-game. The proposed model is composed of three layers: data layer, which stores both dynamic and static data of replay; elements layer, in which a quantitative computational model of fire attack elements is proposed to transform original replay data to features values; correlation mining layer, in which a frequent itemsets construction method is proposed to use three dimensions as terrain, cooperation and damage causality. And with these frequent itemsets, the classic Apriori algorithm can be applied to mine the rules of weapons usage
in the Army contract tactical combat. Finally, the proposed model is applied to the dataset of the second national war-game competition. The experimental results successfully explored the e ectiveness of tanks, vehicles and infantry in di erent battle terrain, as well as the rule of coordination between di erent combat units and weapon strike e ectiveness against di erent targets.
邢思远, 倪晚成, 张海东, 闫科. 基于兵棋复盘数据的武器效用挖掘[J]. 指挥与控制学报, 2020, 6(2): 132-140.
XING Si-Yuan, NI Wan-Cheng, ZHANG Hai-Dong, YAN Ke. Mining of Weapon Utility Based on the Replay Data of War-Game. Journal of Command and Control, 2020, 6(2): 132-140.