Application progress of artificial intelligence in military confrontation

ZHANG Zhi-min; SHI Fei-fei; WAN Yue-liang; XU Yang; ZHANG Fan; NING Huan-sheng

doi:10.13374/j.issn2095-9389.2019.11.19.001

Volume 42 Issue 9

Sep. 2020

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Article Navigation > Chinese Journal of Engineering > 2020 > 42(9): 1106-1118

ZHANG Zhi-min, SHI Fei-fei, WAN Yue-liang, XU Yang, ZHANG Fan, NING Huan-sheng. Application progress of artificial intelligence in military confrontation[J]. Chinese Journal of Engineering, 2020, 42(9): 1106-1118. doi: 10.13374/j.issn2095-9389.2019.11.19.001

Citation:

ZHANG Zhi-min, SHI Fei-fei, WAN Yue-liang, XU Yang, ZHANG Fan, NING Huan-sheng. Application progress of artificial intelligence in military confrontation[J]. Chinese Journal of Engineering, 2020, 42(9): 1106-1118. doi: 10.13374/j.issn2095-9389.2019.11.19.001

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Application progress of artificial intelligence in military confrontation

doi: 10.13374/j.issn2095-9389.2019.11.19.001

ZHANG Zhi-min^{1, 2},
SHI Fei-fei¹,
WAN Yue-liang^{3, 4},
XU Yang¹,
ZHANG Fan¹,
NING Huan-sheng^{1, 4
,
,}

1.
School of Computer & Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Shunde Graduate School of University of Science and Technology Beijing, Foshan 528300, China
3.
Run Technologies Company, Ltd., Beijing 100192, China
4.
Beijing Engineering Research Center for Cyberspace Data Analysis and Applications, Beijing 100083, China

More Information

Corresponding author: E-mail: ninghuansheng@ustb.edu.cn
Received Date: 2019-11-19
Available Online: 2022-10-14
Publish Date: 2020-09-20

Abstract

Abstract

Artificial intelligence (AI), especially the rapid development of deep learning, has a profound impact on various industries and has continuously changed the traditional production methods and lifestyles. From passive learning with computing power to autonomous learning and enhanced learning, the development of machine intelligence is largely due to the innovation of the AI theory and practice. AI has also had a far-reaching impact on the military field, as it has provided modern warfare with new features such as intelligence, interconnectedness, and destructiveness. Winning in a military confrontation requires not only machine intelligence but also human wisdom. Therefore, human-machine collaboration would combine the strengths and complement the weaknesses of human and machine, which is the key to victory in the increasingly complex war environment. How to achieve a high degree of hybrid human–artificial intelligence to obtain a good result of “1+1>2” is also a problem that needs to be further explored in military confrontation. This paper reviewed the application of AI in military confrontation as the starting point and highlighted the important measures and achievements of representative countries in the use of AI technology in the military development process. Moreover, we analyzed the development status from the two perspectives of confrontation strategy and the three-tier architecture of the Internet of Things, revealed the shortcomings of using AI in the current military field, and analyzed the development trend of hybrid human–artificial intelligence in military confrontation. We also presented three possible technical schemes and detailed explanations and finally proposed future research directions. We believe that the future development trend of intelligent military may be based on the hybrid human–artificial intelligence, which will further improve the adaptability of machines to the combat environment and reveal the merits of the integration of human wisdom and machine intelligence; this integration may be the next step of AI research in military confrontation.
- artificial intelligence,
- military confrontation,
- Internet of Things,
- hybrid human–artificial intelligence,
- technical schemes

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References(51)

References

[1]	Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets. Neural Comput, 2006, 18(7): 1527 doi: 10.1162/neco.2006.18.7.1527
[2]	Silver D, Schrittwieser J, Simonyan K, et al. Mastering the game of Go without human knowledge. Nature, 2017, 550(7676): 354 doi: 10.1038/nature24270
[3]	肖占中, 劉昱旻. 智能武器與無人戰爭. 北京: 軍事誼文出版社, 2001 Xiao Z Z, Liu Y M. Intelligent Weapons and Unmanned War. Beijing: Military Friendship Press, 2001
[4]	王雪誠. 人工智能算法: 改寫戰爭的無形之手. 軍事文摘, 2017(21):19 Wang X C. Artificial intelligence algorithm: the invisible hand of rewriting war. Military Digest, 2017(21): 19
[5]	Lecuyer M, Atlidakis V, Geambasu R, et al. Certified robustness to adversarial examples with differential privacy // 2019 IEEE Symposium on Security and Privacy (SP). San Francisco, 2019
[6]	Papernot N, McDaniel P, Wu X, et al. Distillation as a defense to adversarial perturbations against deep neural networks//2016 IEEE Symposium on Security and Privacy (SP). San Jose, 2016
[7]	Liu X Q, Li Y, Wu C R, et al. Adv-BNN: improved adversarial defense through robust bayesian neural network // International Conference on Learning Representations. New Orleans, 2019
[8]	史忠植. 智能主體及其應用. 北京: 科學出版社, 2002 Shi Z Z. Intelligent Agent and Its Application. Beijing: Science Press, 2002
[9]	Desai J P, Ostrowski J, Kumar V. Controlling formations of multiple mobile robots//1998 IEEE International Conference on Robotics and Automation. Belgium, 1998: 2864
[10]	Desai J P, Ostrowski J P, Kumar V. Modeling and control of formations of nonholonomic mobile robots. IEEE Trans Robot Autom, 2001, 17(6): 905 doi: 10.1109/70.976023
[11]	Desai J P. A graph theoretic approach for modeling mobile robot team formations. J Robot Syst, 2002, 19(11): 511 doi: 10.1002/rob.10057
[12]	Das A K, Fierro R, Kumar V, et al. A vision-based formation control framework. IEEE Trans Robot Autom, 2002, 18(5): 813 doi: 10.1109/TRA.2002.803463
[13]	王軍, 趙曉哲, 張瑛涵, 等. 基于免疫多智能體的艦艇編隊協同防空體系模型. 系統仿真學報, 2012, 24(2):263 Wang J, Zhao X Z, Zhang Y H, et al. Cooperative air-defense system of system model for surface warship formation based on immune multi-agent. J Syst Simul, 2012, 24(2): 263
[14]	Sahal M, Agustinah T, Jazidie A. Switching formation and topology in cooperative multi-agent source seeking using gradient estimation // 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT). Yogyakarta, 2019: 151
[15]	Liu L, Liang X L, Zhu C C, et al. Distributed cooperative control for UAV swarm formation reconfiguration based on consensus theory // 2017 2nd International Conference on Robotics and Automation Engineering (ICRAE). Shanghai, 2017: 264
[16]	呂洋, 康童娜, 潘泉, 等. 無人機感知與規避: 概念、技術與系統. 中國科學: 信息科學, 2019, 49(5):520 doi: 10.1360/N112018-00318 Lü Y, Kang T N, Pan Q, et al. UAV sense and avoidance: concepts, technologies and systems. Sci Sin Inform, 2019, 49(5): 520 doi: 10.1360/N112018-00318
[17]	韓靜雅, 王宏倫, 劉暢, 等. 基于視覺的無人機感知與規避系統設計. 戰術導彈技術, 2014(5):11 Han J Y, Wang H L, Liu C, et al. Vision-based system design for UAV target detection and avoidance. Tactical Missile Technol, 2014(5): 11
[18]	李耀軍, 潘泉, 楊峰, 等. 基于多源信息融合的無人機感知與規避研究 // 第二十九屆中國控制會議論文集. 北京, 2010: 2861 Li Y J, Pan Q, Yang F, et al. Research on UAV perception and avoidance based on multi-source information fusion // Proceedings of the 29th China Control Conference. Beijing, 2010: 2861
[19]	黃劍平. 地空導彈部隊在反空襲作戰中的威脅評估研究[學位論文]. 廈門: 廈門大學, 2009 Huang J P. Study on Threat Assessment of Surface to Air Missile Forces in Anti-Air Attack Operations[Dissertation]. Xiamen: Xiamen University, 2009
[20]	Azan Basallo Y, Estrada Senti V, Martinez Sanchez N. Artificial intelligence techniques for information security risk assessment. IEEE Latin America Trans, 2018, 16(3): 897 doi: 10.1109/TLA.2018.8358671
[21]	杜戈, 韓增奇, 李寧霞, 等. 基于神經網絡和遺傳算法的信息傳輸安全風險度評估模型. 情報雜志, 2010, 29(增刊1): 207 Du G, Han Z Q, Li N X, et al. Risk assessment model of information transmission security based on neural network and genetic algorithm. J Intell, 2010, 29(Suppl 1): 207
[22]	孫曉舟, 劉露. 地面無人作戰系統及環境感知雷達發展概述. 科技視界, 2017(8):1 doi: 10.3969/j.issn.2095-2457.2017.08.001 Sun X Z, Liu L. Development of unmanned ground combat system and environmental sensing radar. Sci Technol Vision, 2017(8): 1 doi: 10.3969/j.issn.2095-2457.2017.08.001
[23]	李波, 任紅梅, 肖志河. 人工智能在雷達應用中的限制和發展前景. 軍事文摘, 2019(3):42 Li B, Ren H M, Xiao Z H. Limitation and development prospect of artificial intelligence in radar application. Military Digest, 2019(3): 42
[24]	Yang F, Wang Q, Wu Z D. Cooperative combat system action planning method based on multi-agent system // 2010 Second International Workshop on Education Technology and Computer Science. Wuhan, 2010: 490
[25]	Liu Y F, Zhang A. Multi-agent system and its application in combat simulation // 2008 International Symposium on Computational Intelligence and Design. Wuhan, 2008: 448
[26]	Brodie M L, 崔靖. 未來的智能信息系統: AI與DB技術的結合. 計算機科學, 1989(3):23 Brodie M L, Cui J. Future intelligent information system: the combination of AI and DB technology. Comput Sci, 1989(3): 23
[27]	Nihalani N, Silakari S, Motwani M. Integration of artificial intelligence and database management system: An inventive approach for intelligent databases // 2009 First International Conference on Computational Intelligence, Communication Systems and Networks. Indore, 2009: 35
[28]	邵軍, 吳華, 陳蕾. 基于數據處理相關技術的軍事信息中心構建. 微計算機信息, 2006, 22(3):89 doi: 10.3969/j.issn.1008-0570.2006.03.032 Shao J, Wu H, Chen L. Construction of military information center based on correlation techniques of data processing. Microcomput Inform, 2006, 22(3): 89 doi: 10.3969/j.issn.1008-0570.2006.03.032
[29]	牛志一. 現代化戰爭中的多傳感器信息融合技術研究. 計算機與信息技術, 2006(3):71 Niu Z Y. Research on multi-sensor information fusion technology in modern war. Comput Inform Technol, 2006(3): 71
[30]	Yang X, Shan W, Jia L. Technology of situation awareness based on radar network in cyberspace //Proceedings of the 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing. Beijing, 2013: 1505
[31]	孔亦思, 胡曉峰, 朱豐, 等. 戰場態勢感知中的注意力機制探析. 系統仿真學報, 2017, 29(10):2233 Kong Y S, Hu X F, Zhu F, et al. Attention mechanism in battlefield situation awareness. J Syst Simul, 2017, 29(10): 2233
[32]	元器件交易網. 專家呼吁提升傳感器安全層級. 電腦與電信, 2014, 11(11):21 doi: 10.3969/j.issn.1008-6609.2014.11.015 www.cecb2b.com. Experts call for a higher level of sensor safety. Comput Telecommun, 2014, 11(11): 21 doi: 10.3969/j.issn.1008-6609.2014.11.015
[33]	陳雷. 物聯網中認證技術與密鑰管理的研究[學位論文]. 長沙: 中南大學, 2013 Chen L. Research on Authentication Technology and Key Management in Internet of Things[Dissertation]. Changsha: Central South University, 2013
[34]	Du W L, Wang R H, Ning P. An efficient scheme for authenticating public keys in sensor networks // Proceedings of the 6th ACM International Symposium on Mobile Ad Hoc Networking & Computing. Urbana-Champaign IL, 2005: 58
[35]	Ghormare S, Sahare V. Implementation of data confidentiality for providing high security in Wireless Sensor Network // 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS). Coimbatore, 2015: 1
[36]	Gandhimathi L, Murugaboopathi G. Cross layer intrusion detection and prevention of multiple attacks in Wireless Sensor Network using Mobile agent // 2016 International Conference on Information Communication and Embedded Systems (ICICES). Chennai, 2016: 1
[37]	杜志強, 沈玉龍, 馬建峰, 等. 基于信息覆蓋的無線傳感器網絡訪問控制機制. 通信學報, 2010, 31(2):113 doi: 10.3969/j.issn.1000-436X.2010.02.017 Du Z Q, Shen Y L, Ma J F, et al. Two-hop cover-based access control scheme for wireless sensor networks. J Commun, 2010, 31(2): 113 doi: 10.3969/j.issn.1000-436X.2010.02.017
[38]	陳婷, 盧建朱, 江俊暉. 一種具有強匿名性的無線傳感器網絡訪問控制方案. 計算機工程, 2015, 41(1):126 doi: 10.3969/j.issn.1000-3428.2015.01.023 Chen T, Lu J Z, Jiang J H. An access control scheme with strong anonymity in wireless sensor network. Comput Eng, 2015, 41(1): 126 doi: 10.3969/j.issn.1000-3428.2015.01.023
[39]	金寧, 張道遠, 高建橋, 等. 對稱密碼和非對稱密碼算法在無線傳感器網絡中應用研究. 傳感技術學報, 2011, 24(6):874 doi: 10.3969/j.issn.1004-1699.2011.06.019 Jin N, Zhang D Y, Gao J Q, et al. A study on the application of symmetric ciphers and asymmetric ciphers in wireless sensor networks. Chin J Sens Actuators, 2011, 24(6): 874 doi: 10.3969/j.issn.1004-1699.2011.06.019
[40]	Akila V, Sheela T. Preserving data and key privacy in data aggregation for wireless sensor networks // 2017 2nd International Conference on Computing and Communications Technologies (ICCCT). Chennai, 2017: 282
[41]	趙晨. 混沌序列密碼在無線傳感器網絡中的應用研究[學位論文]. 北京: 北京化工大學, 2014 Zhao C. Research on the Application of Chaotic Sequence Cipher in Wireless Sensor Network[Dissertation]. Beijing: Beijing University of Chemical Technology, 2014
[42]	毛琳, 鄧自立. 多傳感器信息融合Wiener反卷積預報器 // 第二十六屆中國控制會議論文集. 張家界, 2007: 1013 Mao L, Deng Z L. Multisensor information fusion wiener deconvolution predictor //2007 26th Chinese Control Conference. Zhangjiajie, 2007: 1013
[43]	李念國, 趙慧. 無人機多傳感器數據融合的設計要求. 國防科技, 2015, 36(5):52 Li N G, Zhao H. The requirements on the data fusion of multiple-sensor of UAV. Natl Defense Sci Technol, 2015, 36(5): 52
[44]	Ruzzo F, Ramponi G. Fuzzy methods for multisensor data fusion // 1993 IEEE Instrumentation and Measurement Technology Conference. Irvine, 1993: 676
[45]	Jimenez L O, Morales-Morell A, Creus A. Classification of hyperdimensional data based on feature and decision fusion approaches using projection pursuit, majority voting, and neural networks. IEEE Trans Geosci Remote Sens, 1999, 37(3): 1360 doi: 10.1109/36.763300
[46]	Bass T, Gruber D. A glimpse into the future of id[J/OL]. USENIX (2001-2-1)[2019-11-19]. http://pdfs.semanticscholar.org/7ac9/8c4f3b72210775b08aa5849d5501de9c7048.pdf
[47]	Tsochev G, Trifonov R, Yoshinov R, et al. Some security model based on multi agent systems //2018 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO). Prague, 2018: 32
[48]	任偉, 蔣興浩, 孫錟鋒. 基于RBF神經網絡的網絡安全態勢預測方法. 計算機工程與應用, 2006, 42(31):136 doi: 10.3321/j.issn:1002-8331.2006.31.041 Ren W, Jiang X H, Sun T F. RBFNN-based prediction of networks security situation. Comput Eng Appl, 2006, 42(31): 136 doi: 10.3321/j.issn:1002-8331.2006.31.041
[49]	張翔, 胡昌振, 劉勝航, 等. 基于支持向量機的網絡攻擊態勢預測技術研究. 計算機工程, 2007, 33(11):10 doi: 10.3969/j.issn.1000-3428.2007.11.004 Zhang X, Hu C Z, Liu S H, et al. Research on network attack situation forecast technique based on support vector machine. Comput Eng, 2007, 33(11): 10 doi: 10.3969/j.issn.1000-3428.2007.11.004
[50]	任崇廣. 面向海量數據處理領域的云計算及其關鍵技術研究[學位論文]. 南京: 南京理工大學, 2013 Ren C G. Research on Cloud Computing and Its Key Technologies for Massive Data Processing[Dissertation]. Nanjing: Nanjing University of Technology, 2013
[51]	褚秋雯. 從哲學的角度看人工智能[學位論文]. 武漢: 武漢理工大學, 2014 Chu Q W. On Artificial Intelligence from the Perspective of Philosophy[Dissertation]. Wuhan: Wuhan University of Technology, 2014