从态势认知走向态势智能认知

doi:10.16182/j.issn1004731x.joss.201803001

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (3): 761-771.doi: 10.16182/j.issn1004731x.joss.201803001

• 专栏：态势智能认知仿真 • 下一篇

从态势认知走向态势智能认知

朱丰^1,2, 胡晓峰¹, 吴琳¹, 贺筱媛¹, 吕学志¹, 廖鹰^1,3

1.中国人民解放军国防大学,北京 100091;
2.中国人民解放军93682部队,北京 101300;
3.中国人民解放军信息工程大学,郑州 450001

收稿日期:2018-01-07 出版日期:2018-03-08 发布日期:2019-01-02
作者简介:朱丰(1983-),男,北京,博士后,工程师,研究方向为战场态势认知与辅助决策分析、体系效能评估、深度学习理论与方法。
基金资助:
国家自然科学基金(61374179,61773399),军民共用重大研究计划联合基金(U1435218),国家自然科学基金青年科学基金(61703412)

From Situation Cognition Stepped into Situation Intelligent Cognition

Zhu Feng^1,2, Hu Xiaofeng¹, Wu Lin¹, He Xiaoyuan¹, Lü Xuezhi¹, Liao Ying^1,3

1.National Defense University of PLA, Beijing 100091, China;
2.No.93682 Unit of PLA, Beijing 101300, China;
3.Information Engineering of PLA, Zhengzhou 450001, China

Received:2018-01-07 Online:2018-03-08 Published:2019-01-02

摘要/Abstract

摘要： 针对联合作战背景下战役级作战态势认知相关问题进行了探讨,提出了态势认知、态势智能认知的概念模型,并厘清了若干概念;提出了态势智能认知相关技术框架,并剖析了其中要重点突破的5个关键问题,给出了可能的技术路线,为从作战态势认知走向态势智能认知奠定了基础。

关键词: 战场态势认知, 人工智能技术, 态势智能认知, 概念模型, 技术框架

Abstract: Aimed at operational situation cognition and some relevant problems under the background of the Joint-Tactical, some deep researches are carried out in this paper. The concept models of combat situation cognition and situation intelligent cognition are proposed respectively, and some related concepts are clarified. The situation intelligent cognition technology framework is proposed, and five key problems which should be solved are analyzed, and the possible technical routes are given. These research contents and achievements build a foundation for stepping into the situation intelligent cognition from combat situation cognition.

Key words: combat situation cognition, artificial intelligence, situation intelligent cognition, concept model, technology framework

中图分类号:

E917

朱丰, 胡晓峰, 吴琳, 贺筱媛, 吕学志, 廖鹰. 从态势认知走向态势智能认知[J]. 系统仿真学报, 2018, 30(3): 761-771.

Zhu Feng, Hu Xiaofeng, Wu Lin, He Xiaoyuan, Lü Xuezhi, Liao Ying. From Situation Cognition Stepped into Situation Intelligent Cognition[J]. Journal of System Simulation, 2018, 30(3): 761-771.

参考文献 29

[1]	宋元, 王永春. 海上防空作战态势估计理论及应用[M]. 北京: 国防工业出版社, 2014. SONG Yuan, WANG Yongchun. Theory and Application of Situation Assessment for Naval Air Defense[M]. Beijing: National Defense Industry Press, 2014.
[2]	张最良, 黄谦. 进一步推进我军军事运筹学研究与应用的创新[J]. 军事运筹与系统工程, 2014, 28(4): 72-76. ZHANG Zuiliang, HUANG Qian. Farther advancing military operation research and application innovation of our military[J]. Military operation research and systems engineering, 2014, 28(4): 72-76.
[3]	司光亚, 高翔, 刘洋, 等. 基于仿真大数据的效能评估指标体系构建方法[J]. 大数据, 2016, 2(4): 57-68. SI Guangya, GAO Xiang, LIU Yang, et al. Method for Building effectiveness Evaluation Index System Based on Big Simulation Data[J]. Big Data Research, 2016, 2(4): 57-68.
[4]	习近平. 决胜全面建成小康社会, 夺取新时代中国特色社会主义伟大胜利[R]. 人民日报, 2017-10-19. XI Jinping. Make Overall Efforts to Build a Well-to-do Society and Win the Great Victory of Socialism with Chinese Characteristics in the New Era[R]. People’s Daily, 2017-10-19.
[5]	Fusano A, Sato H, Namatame A.Multi-agent based combat simulation from OODA and network perspective[C]. UkSim 13th International Conference on Computer Modelling and Simulation, 2011: 49-54.
[6]	Endsley M R.Toward a Theory of Situation Awareness in Dynamic Systems[J]. Human Factors: The Journal of the Human Factors and Ergonomics Society (S0018-7208), 1995, 37(1): 32-64.
[7]	王杨. 战场态势目标识别与态势意图预测的算法研究[D]. 无锡: 江南大学, 2015.WANG Yang. Research on Battlefield Target Identification and Situation Intention Forecasting[D]. Wuxi: Jiangnan University Master Thesis, 2015.
[8]	柳玉, 文家焱, 陈建华. 计算机兵棋系统发展及应用研究[J]. 兵工自动化, 2015, 34(8): 20-26. Liu Yu, Wen Jiayan, Chen Jianhua. Development and Application of Computer War Games System[J]. Ordnance Industry Automation, 2015, 34(8): 20-26.
[9]	卜令娟, 刘俊, 邱黄亮, 等. 战场通用态势估计本体模型的构建[C]. 第二届中国指挥控制大会论文集, 北京: 2014: 138-142. BU Lingjuan, LIU Jun, QIU Huangliang, et al. A General Model of Ontology- Based Construction of the Battlefield Situation Assessment[C]. The 2nd China Command and Control Conference, Beijing, 2014: 138-142.
[10]	朱丰, 胡晓峰. 基于深度学习的战场态势评估综述与研究展望[J]. 军事运筹与系统工程, 2016, 30(3): 22-27. ZHU Feng, HU Xiaofeng. Battle Situation Assessment Summarization and Research Perspective Based on Deep Learning[J]. Military Operations Research and Systems Engineering, 2016, 30(3): 22-27.
[11]	朱丰, 胡晓峰, 吴琳, 等. 基于深度学习的指挥员战场态势高级理解思维过程探索性模拟方法[J]. 火力与指挥控制, 2018. ZHU Feng, Hu Xiaofeng, WU Lin, et al. Exploring Simulation Method of Thinking Process of Battlefields Situation Senior Comprehension for Commanders Based on Deep Learning[J]. Fire Control and Command Control, 2018.
[12]	朱丰, 胡晓峰, 贺筱媛, 等. 一种基于CNN的样本不足战场包围态势认知方法[J]. 系统仿真学报, 2017, 29(10): 2291-2300. ZHU Feng, Hu Xiaofeng, HE Xiaoyuan, et al. Cognition Method for Battlefields Encompassing Situation Based on Convolution Neural Network without Enough Samples[J]. Journal of System Simulation, 2017, 29(10): 2291-2300.
[13]	Castellano G, Cimino M G C A, Fanelli A M, et al. A Multi-Agent System for Enabling Collaborative Situation Awareness via Position-Based Stigmergy and Neuro- Fuzzy Learning[J]. Neurocomputing (S0925-2312), 2014, 135(13): 86-97.
[14]	Elpiniki I Papageorgiou, Jose L Salmeron.A Review of Fuzzy Cognitive Maps Research during the Last Decade[J]. IEEE Transactions on Fuzzy Systems (S1063-6706), 2013, 21(1): 66-79.
[15]	Bruce N D B. Saliency, attention and visual search: an information theoretic approach[J]. Journal of Vision (Online)(S1534-7362), 2009, 9(3): 1-24.
[16]	许世勇, 王家胜. 智能化作战正在掀起一场新的军事革命[R]. 手机搜狐 (2018-01-06). XU Shiyong, WANG Jiasheng. Intelligent Warfare is Setting off a New Military Revolution[R]. Sohu Com Mobile (2018-01-06).
[17]	杨蓓, 缑西梅, 艾艳. 专家系统中的模糊知识表示及推理研究[J]. 郑州大学学报(理学版), 2004, 36(2): 31-33. Yang Bei, Gou Ximei, Ai Yan. Study on the Fuzzy Knowledge Representation and Reasoning in Expert System[J]. Journal of Zhengzhou University (Natural Science Edition), 2004, 36(2): 31-33.
[18]	郭晓波, 赵书良, 刘军丹, 等. 基于概念图的关联规则知识表示[J]. 计算机科学, 2013, 40(8): 261-265. GUO Xiaobo, ZHAO Shuliang, LIU Jundan, et al. Knowledge Presentation of Association Rules Based on Conceptual Graphs[J]. Computer Science, 2013, 40(8): 261-265.
[19]	刘知远, 孙茂松, 林衍凯, 等. 知识表示学习研究进展[J]. 计算机研究与发展, 2016, 53(2): 247-261. Liu Zhiyuan, Sun Maosong, Lin Yankai, et al. Knowledge Representation Learning: A Review[J]. Journal of Computer Research and Development, 2016, 53(2): 247-261.
[20]	马创新. 论知识表示[J]. 现代情报, 2014, 34(3): 21-26. Ma Chuangxin. The Research of Knowledge Representation[J]. Journal of Modern Information, 2014, 34(3): 21-26.
[21]	唐晓琳. CCF2016-2017中国计算机科学技术发展报告[M]. 北京: 机械工业出版社, 2017.TANG Xiaolin. CCF2016-2017 China Computer Science and Technology Development Report[M]. Beijing: China Machine Press, 2017.
[22]	张俊林. 深度学习中的注意力机制[R]. 微信文章《人工智能头条》(2017-11-02). ZHANG Junlin. Attention Mechanisms in Deep Learning [R]. Weixin Paper ’Artificial Intelligence Headlines’(2017-11-02).
[23]	尹宝才, 王文通, 王立春. 深度学习研究综述[J]. 北京工业大学报, 2015, 41(1): 48-59. YIN Baocai, WANG Wentong, WANG Lichun. Review of Deep Learning[J]. Journal of Beijing University of Technology, 2015, 41(1): 48-59.
[24]	Matej Moravcik, Martin Schmid, Neil Burch, et al. DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker[J]. arXiv:1701.01724v1 (2017-01-06).
[25]	Peng P, Wen Y, Yang Y, et al. Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games[J]. arXiv:1703.10069v4 (2017-09-14).
[26]	Brenden M Lake, Ruslan Salakhutdinov, Joshua B Tenenbaum.Human-Level Concept Learning through Probabilistic Program Induction[J]. Science (S0036-8075), 2015, 350: 1332-1339.
[27]	Pan S J, Yang Q.A Survey on Transfer Learning[J]. IEEE Trans. on Knowledge and Data Engineering (S1041-4347), 2010, 22(10): 1345-1359.
[28]	Goodfellow I, Pouget-Abadie J, Mirza M, et al.Generative Adversarial Nets[C]// Proceedings of the 2014 Conference on Advances in Neural Information Processing Systems 27. Montreal, Canada: Curran Associates, Inc., 2014: 2672-2680.
[29]	高惠琳. 基于卷积神经网络的军事图像分类[J]. 计算机应用研究, 2017, 34(10). (优先出版). Gao Huilin. Military Image Classification Based on Convolutional Neural Network[J]. Application Research of Computers, 2017, 34(10). (First Published.)

从态势认知走向态势智能认知

From Situation Cognition Stepped into Situation Intelligent Cognition

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