Research on Experimental Method of Joint Operation Simulation Based on Human-machine Hybrid Intelligence

doi:10.16182/j.issn1004731x.joss.20-0592

Abstract

Abstract: In view of the difficulties that the joint operation simulation experiment methods are mainly for guiding equipment evaluation and demonstration, which is difficult to effectively support the research of operation problems, a joint operation simulation experiment method based on human-machine hybrid intelligence is proposed. The classification, generation and accumulation process of the knowledge in joint operation simulation experiment are clarified. Through the detailed descriptions of experimental interaction process, experimental operation process, experimental driving mode, simulation operation mode, supporting system structure, etc., a joint operation simulation experiment framework based on man-machine hybrid intelligence is constructed. It provides a new method for analyzing operation capability, innovating operation plan and developing operation concept based on simulation experiment.

Key words: joint operation simulation, experimental method, human-machine hybrid intelligence, reinforcement learning

CLC Number:

E917
TP391.9

Ma Jun, Yang Jingyu, Wu Xi. Research on Experimental Method of Joint Operation Simulation Based on Human-machine Hybrid Intelligence[J]. Journal of System Simulation, 2021, 33(10): 2323-2334.

References

[1] 李伯虎, 柴旭东, 张霖, 等. 面向新型人工智能系统的建模与仿真技术初步研究[J]. 系统仿真学报, 2018, 30(2): 349-362.
Li Bohu, Chai Xudong, Zhang Lin, et al.Preliminary Study of Modeling and Simulation Technology Oriented to Neo-type Artificial Intelligent Systems[J]. Journal of System Simulation, 2018, 30(2): 349-362.
[2] 徐享忠, 郭齐胜. 军用仿真发展现状与展望[J]. 装甲兵工程学院学报, 2019, 33(1): 79-90.
Xu Xiangzhong, Guo Qisheng.Development Status and Perspective of Military Simulation[J]. Journal of Academy of Armored Force Engineering, 2019, 33(1): 79-90.
[3] 李智军, 石光明, 杨辰光, 等. 人机混合智能专题简介[J]. 中国科学: 信息科学, 2019, 49(5): 144-146.
Li Zhijun, Shi Guangming, Yang Chenguang, et al.Special Focus on Human-Robot Hybrid Intelligence[J]. Science China Information Science, 2019, 49(5): 144-146.
[4] 李梦汶. 联合作战仿真实验的几个基本问题[J]. 军事运筹与系统工程, 2008, 22(1): 25-29.
Li Mengwen.Some Basic Problems of Joint Operation Simulation Experiment[J]. Military Operations Research and Systems Engineering, 2008, 22(1): 25-29.
[5] Volodymyr M, Koray K, David S, et al.Human-level Control Through Deep Reinforcement Learning[J]. Nature (S1476-4687), 2015, 518(7540): 529.
[6] D Silver, Huang A, Maddison C J, et al. Mastering the game of Go with deep neural networks and tree search[J]. Nature (S1476-4687), 2016, 529(7587): 484-489.
[7] Silver D, Schrittwieser J, Simonyan K, et al.Mastering the game of Go without human knowledge. Mastering the game of go without human knowledge[J]. Nature (S1476-4687), 2017, 550(7676): 354-359.
[8] Grandmaster level in StarCraft II Using Multi-agent Reinforcement Learn-ing[EB/OL]. (2019-10-30) [2020-05-21]. https://www.nature.com/articles/s41586- 019-1724-z.
[9] 李晨溪, 曹雷, 张永亮, 等. 基于知识的深度强化学习研究综述[J]. 系统工程与电子技术, 2017, 39(11): 217-227.
Li Chenxi, Cao Lei, Zhang Yongliang, et al.Knowledge-based Deep Reinforcement Learning: A Review[J]. Systems Engineering and Electronics, 2017, 39(11): 217-227.
[10] 李平, 杨政银. 人机融合智能: 人工智能3.0[J]. 清华管理评论, 2018, 63(增2): 74-83.
Li Ping, Yang Zhengyin.Human-computer Fusion Intelligence: AI3.0[J]. Tsinghua Business Review, 2018, 63(S2): 74-83.
[11] 刘伟, 张玉坤, 曹国熙. 有关军事人机混合智能的几点思考[J]. 火力与指挥控制, 2018, 43(10): 1-7.
Liu Wei, Zhang Yukun, Cao Guoxi.Thoughts on Military Human-machine Hybrid Intelligence[J]. Fire Control & Command Control, 2018, 43(10): 1-7.
[12] 张昱, 杨镜宇, 李仁见, 等. 面向武器装备体系仿真的五环聚焦实验框架[J]. 系统仿真学报, 2014, 26(11): 2541-2545.
Zhang Yu, Yang Jingyu, Li Renjian, et al.5-Loop Framework of Making and Converging Experiments for Simulation of Weapon System of Systems[J]. Journal of System Simulation, 2014, 26(11): 2541-2545.
[13] 胡润涛, 胡晓峰. 基于数据耕耘的探索性仿真分析框架设计[J]. 计算机仿真, 2009, 26(1): 17-19, 88.
Hu Runtao, Hu Xiaofeng.The Design of Exploratory Simulation Analysis Framework Based Data Farming[J]. Computer simulation, 2009, 26(1): 17-19, 88.
[14] 李斌, 李春洪, 刘苏洋. 基于数据耕耘的探索性仿真实验框架研究[J]. 指挥控制与仿真, 2011, 33(4): 57-62.
Li Bin, Li Chunhong, Liu Suyang.Research on the Framework of Exploratory Simulation Experiment Based on Data Farming[J]. Command Control & Simulation, 2011, 33(4): 57-62.
[15] 代海峰, 毕义明, 韩慧华, 等. 基于数据耕耘的作战仿真评估系统框架设计[J]. 火力与指挥控制, 2019, 44(7): 131-141.
Dai Haifeng, Bi Yiming, Han Huihua, et al.Framework Design of Combat Simulation Evaluation System Based on Data Farming[J]. Fire Control & Command Control, 2019, 44(7): 131-141.