面向智能化兵棋的认知行为建模方法研究

doi:10.16182/j.issn1004731x.joss.21-0727

系统仿真学报 ›› 2021, Vol. 33 ›› Issue (9): 2037-2047.doi: 10.16182/j.issn1004731x.joss.21-0727

所属专题：特约稿件

• 专栏：下一代兵棋系统研究 • 上一篇下一篇

面向智能化兵棋的认知行为建模方法研究

贺筱媛, 郭圣明, 吴琳, 李东, 许霄, 李丽

国防大学联合作战学院,北京 100091

收稿日期:2021-07-21 修回日期:2021-07-27 出版日期:2021-09-18 发布日期:2021-09-17
作者简介:贺筱媛(1968-)，女，博士，高工，研究方向为军事运筹与智能决策。E-mail：bingling1922@126.com
基金资助:
军民共用重大研究计划联合基金(U1435218)

Modeling Research of Cognition Behavior for Intelligent Wargaming

He Xiaoyuan, Guo Shengming, Wu Lin, Li Dong, Xu Xiao, Li Li

College of Joint Operations, National Defence University, Beijing 100091, China

Received:2021-07-21 Revised:2021-07-27 Online:2021-09-18 Published:2021-09-17

摘要/Abstract

摘要： 针对智能化兵棋系统建设运用过程中的认知行为建模问题,提出一种基于角色-行动-场景(Actor-Operations-Scene, AOS)的指挥决策智能体认知行为建模框架,探索了场景知识图谱服务、智能态势认知、智能决策规划和自主综合控制等关键认知行为模型的实现思路和方法,为智能化兵棋系统建设及运用提供可行技术方案。

关键词: 智能化兵棋, 指挥决策智能体, 认知建模, 态势认知, 决策规划, 综合控制

Abstract: Aiming at the problem of cognitive behavior modeling in the construction and application of intelligent wargaming system, one modeling framework based on Actor-Operations-Scene (AOS) was proposed for C2 agent in wargaming. Then the realization of cognitive behavior modeling method for C2 agent was explored, including the modeling for the scenario oriented knowledge graph, intelligent situation awareness, operational planning, integrated operational control. It provides a feasible scheme for the construction and application of intelligent wargaming system.

Key words: intelligent wargaming, agent for command and control, cognition behavior modeling, situation awareness, operational planning, integrated operational control

中图分类号:

TP391.9

贺筱媛, 郭圣明, 吴琳, 李东, 许霄, 李丽. 面向智能化兵棋的认知行为建模方法研究[J]. 系统仿真学报, 2021, 33(9): 2037-2047.

He Xiaoyuan, Guo Shengming, Wu Lin, Li Dong, Xu Xiao, Li Li. Modeling Research of Cognition Behavior for Intelligent Wargaming[J]. Journal of System Simulation, 2021, 33(9): 2037-2047.

参考文献

[1] 胡晓峰, 司光亚, 吴琳, 等. 战争模拟原理与系统[M]. 北京: 国防大学出版社, 2009: 386-387.
Hu Xiaofeng, Si Guangya, Wu Lin, et al.War Gaming & Simulation Principle and System[M]. Beijing: National Defense University Press, 2009: 386-387.
[2] 胡晓峰, 贺筱媛, 陶九阳. AlphaGo的突破与兵棋推演的挑战[J]. 科技导报, 2017, 35(21): 49-60.
Hu Xiaofeng, He Xiaoyuan, Tao Jiuyang.AlphaGo's Breakthrough and Challenges of Wargaming[J]. Science & Technology Review, 2017, 35(21): 49-60.
[3] Pew R W, Mavor A S.Modeling Human and Organizational Behavior: Application to Military Simulations[M]. Washington, DC: National Academy Press, 1998: 1-19.
[4] Reece D, Brett B, Franceschini D, et al.ModSAF as a Model of Cognition[C]//Eleventh Conference on Computer- Generated Forces and Behavior Representation. SAIC 12901 Science Drive Orlando, FL, 2002:325-335.
[5] Courtemanche A J, Wittman R L .OneSAF: A Product Line Approach for a Next-Generation CGF[C]//Eleventh Conference on Computer-Generated Forces and Behavior Representation. SAIC 12901 Science Drive Orlando, FL, 2002: 32826-33014.
[6] Salisbury Marnie R, Seidel David W, Booker Lashon B.A Brief Review of the Command Forces (CFOR) Program[C]//Winter Simulation Conference Proceedings, Orlando, Florida: Institute of Electrical and Electronics Engineers, 1996: 1-8.
[7] Silver D, Huang A, Maddison C J, et al.Mastering the Game of Go with Deep Neural Networks and Tree Search[J]. Nature (S0028-0836), 2016, 529(7587): 484-489.
[8] Schrittwieser J., Antonoglou I., Hubert T.et al.Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model[J]. Nature(S0028-0836), 2020, 588(7839): 604-609.
[9] Brown N, Sandholm T.Superhuman AI for Heads-up No-limit Poker: Libratus Beats Top Professionals[J]. Science (S0036-8075), 2017, 359(6374): 1733.
[10] Vinyals O, Babuschkin I, Czarnecki W M, et al.Grandmaster Level in StarCraft II Using Multi-agent Reinforcement learning[J]. Nature (S0028-0836), 2019, 575(7782): 350-354.
[11] 胡晓峰, 贺筱媛, 陶九阳. 认知仿真:是复杂系统建模的新途径吗?[J]. 科技导报, 2018, 36(12): 46-54.
Hu Xiaofeng, He Xiaoyuan, Tao Jiuyang.Cognitive Simulation: Is It a New Approach for Complex System modeling?[J]. Science & Technology Review, 2018, 36(12): 46-54.
[12] Ulbrich S, Menzel T, Reschka A, et al.Defining and Substantiating the Terms Scene, Situation and Scenario for automated Driving[C]//2015 IEEE 18^th International Conference on Intelligent Transportation Systems. Gran Canaria, Spain: Nstitute of Electrical and Electronics Engineers, 2015: 982-988.
[13] 吴金平, 王永浩, 田立业. 潜艇作战实体认知行为建模研究[C]//第18届中国系统仿真技术及其应用学术年会论文集(18th CCSSTA 2017), 安徽, 合肥: 中国科学技术大学出版社, 2017: 66-70.
Wu Jinping, Wang Yonghao, Tian Liye.Modeling Research of Entity Cognition Behaviors in Submarine Operation[C]// 18th Conference of China System Simulation Technology & Application. Hefei, Anhui: University of Science and Technology of China Press, 2017: 57-61.
[14] 胡晓峰. 战争工程论: 走向信息时代的战争方法学[M].北京: 国防大学出版社, 2013: 1-50.
Hu Xiaofeng.On War System Engineering:Methodology Torwards Information Age's War[M]. Beijing: National Defense University Press, 2013: 1-50.

面向智能化兵棋的认知行为建模方法研究

Modeling Research of Cognition Behavior for Intelligent Wargaming

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