Development Status of Computer Generated Force Behavior Modeling

doi:10.3778/j.issn.1002-8331.1907-0176

Abstract

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

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

GAO Ang, DUAN Li, ZHANG Guohui, DONG Zhiming, CAO Jie, GUO Qisheng. Development Status of Computer Generated Force Behavior Modeling[J]. Computer Engineering and Applications, 2019, 55(19): 43-51.

高昂，段莉，张国辉，董志明，曹洁，郭齐胜. 计算机生成兵力行为建模发展现状[J]. 计算机工程与应用, 2019, 55(19): 43-51.

[1]	HUANG Dongyi, YANG Bing, WU Zihao, KUANG Jiayi, YAN Zeming. Spatio-Temporal Fully Connected Convolutional Neural Networks for Citywide Cellular Prediction [J]. Computer Engineering and Applications, 2021, 57(9): 168-175.
[2]	ZHOU Lungang, SUN Yifeng, WANG Kun, WU Jiang, HUANG Weigui, LI Binglong. End to End Object Recognition Algorithm for Multi-attributes of Multi-values [J]. Computer Engineering and Applications, 2021, 57(9): 182-190.
[3]	ZHANG Cheng, DAI Junfeng, XIONG Wenxin. Improved Handwritten Date Recognition in Scanned Documents Combined with LeNet-5 [J]. Computer Engineering and Applications, 2021, 57(9): 207-211.
[4]	WU Wenjie, SONG Wen’ai, GAO Xuemei, YANG Jijiang, WANG Qing, HUANG Liping, LEI Yi. Review of X-Ray-Based Computer-Aided Diagnosis of Adult OSA [J]. Computer Engineering and Applications, 2021, 57(9): 1-8.
[5]	RAN Rong, XU Xinghua, QIU Shaohua, CUI Xiaopeng, OUYANG Bin. Review of Crack Detection Methods Based on Deep Convolutional Neural Networks [J]. Computer Engineering and Applications, 2021, 57(9): 23-35.
[6]	LI Xiaoxiao, HU Xiaoguang, WANG Ziqiang, DU Zhuoqun. Survey of Instance Segmentation Based on Deep Learning [J]. Computer Engineering and Applications, 2021, 57(9): 60-67.
[7]	LI Mingshan, HAN Qingpeng, ZHANG Tianyu, WANG Daolei. Safety Helmet Detection Method of Improved SSD [J]. Computer Engineering and Applications, 2021, 57(8): 192-197.
[8]	ZENG Chunyan, YAN Kang, WANG Zhifeng, YU Yan, JI Chunmei. Survey of Interpretability Research on Deep Learning Models [J]. Computer Engineering and Applications, 2021, 57(8): 1-9.
[9]	XU Degang, WANG Lu, LI Fan. Review of Typical Object Detection Algorithms for Deep Learning [J]. Computer Engineering and Applications, 2021, 57(8): 10-25.
[10]	JIANG Bin, ZHONG Rui, ZHANG Qiuwen, ZHANG Huanlong. Survey of Non-frontal Facial Expression Recognition by Using Deep Learning Methods [J]. Computer Engineering and Applications, 2021, 57(8): 48-61.
[11]	ZHAO Yuanli, LIANG Zhijian. Research on Stance Detection Based on Dual Attention Mechanism of Heteronuclear Convolution [J]. Computer Engineering and Applications, 2021, 57(8): 119-125.
[12]	LI Jian, SUN Dasong, ZHANG Beiwei. Image Restoration Using Dual-Encoder and Adversarial Training [J]. Computer Engineering and Applications, 2021, 57(7): 192-197.
[13]	YANG Bo, TAO Qingchuan, DONG Peijun. Surgical Instrument Segmentation Method Based on Improved Deeplab v3+ Network [J]. Computer Engineering and Applications, 2021, 57(7): 222-227.
[14]	LIU Di, JIA Jinlu, ZHAO Yuqing, QIAN Yurong. Overview of Image Denoising Methods Based on Deep Learning [J]. Computer Engineering and Applications, 2021, 57(7): 1-13.
[15]	YANG Peiwei, ZHOU Yuhong, XING Gang, TIAN Zhiqiang, XU Xiayu. Applications of Convolutional Neural Network in Biomedical Image [J]. Computer Engineering and Applications, 2021, 57(7): 44-58.

Development Status of Computer Generated Force Behavior Modeling

计算机生成兵力行为建模发展现状

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics