计算机工程与应用 ›› 2019, Vol. 55 ›› Issue (19): 43-51.DOI: 10.3778/j.issn.1002-8331.1907-0176

• 热点与综述 • 上一篇    下一篇

计算机生成兵力行为建模发展现状

高昂,段莉,张国辉,董志明,曹洁,郭齐胜   

  1. 1.陆军装甲兵学院 演训中心,北京 100072
    2.中国人民解放军61516部队
  • 出版日期:2019-10-01 发布日期:2019-09-30

Development Status of Computer Generated Force Behavior Modeling

GAO Ang, DUAN Li, ZHANG Guohui, DONG Zhiming, CAO Jie, GUO Qisheng   

  1. 1.Department of Drill and Training, Army Academy of Armored Forces, Beijing 100072, China
    2. Unit  61516 of PLA, China
  • Online:2019-10-01 Published:2019-09-30

摘要: 体系仿真是解决目前信息化条件下诸多军事问题的有效手段,仿真系统中计算机生成兵力(Computer Generated Force,CGF)行为表示准确与否是直接影响军事问题研究结论的重要因素。针对目前CGF自治性难以满足军事问题研究需求,系统总结了10年来CGF行为建模方法,并对比分析了不同方法的优缺点,梳理了近5年国内外CGF行为建模技术发展现状,针对军事问题研究需求,对当前该领域存在的问题和发展前景进行总结,并提出四种CGF行为建模思想方法。

关键词: 计算机生成兵力, 行为建模, 贝叶斯网络, 深度学习, 强化学习, 知识图谱

Abstract: System simulation is an effective means to solve many military problems under the condition of informationization. In the simulation system, the accuracy of computer generated force behavior is an important factor that affects the conclusion of military research. Considering that the autonomy of CGF is difficult to meet the needs of military research, CGF behavior modeling methods in the past 10 years are systematically summarized, the advantages and disadvantages of different methods are compared and analyzed, the development status of CGF behavior modeling methods at home and abroad in recent five years is summarized. Aiming at the research demand of military issues, the existing problems and development prospects in this field are summarized and prospected, and six CGF behavior modeling thinking methods are proposed.

Key words: computer generated force, behavior modeling, Bayesian networks, deep learning, reinforcement learning, knowledge graph