Method for Dynamic Coalition Formation of Wargame Agent for Force Cooperation

doi:10.16182/j.issn1004731x.joss.24-0058

Abstract

Abstract:

Regarding the issue of cooperative task alliance formation and adjustment in multi-agent dynamic confrontation scenarios at the tactical level, this method comprehensively considers factors such as target value, task allocation, and operator characteristics, as well as the benefits and costs of executing different types of tasks. we propose a targeted force coordination adjustment for dynamic task alliance formation based on behavioral constraints. The $“$ MiaoSuan-Wise Winning Instant Strategy Human-Computer Confrontation Platform $”$ of Chinese Academy of Sciences (CAS) is used as an experimental platform to conduct confrontation experiments. The experiment demonstrates that the proposed method improves the dynamic coordination ability of heterogeneous multi-agents during the system confrontation process, thereby enhancing the confrontation success rate.

Key words: multi-agent systems, wargame confrontation, alliance game, task allocation, intelligent wargame

CLC Number:

TP18

Yao Changhua, Bi Shanning, Ma Rufei, Yu Xiaohan, Li Jiaqiang, Chen Jinli. Method for Dynamic Coalition Formation of Wargame Agent for Force Cooperation[J]. Journal of System Simulation, 2025, 37(5): 1188-1196.

Figures/Tables 10

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Table 1

Fig. 6

Fig. 7

Table 2

Fig. 8

References 15

1	Kozioł Mariusz. Interoperability and Data Flow Between JTLS-GO Simulation System and LOGFAS Logistic System During CAX (Computer Assisted Exercise) Exercises[J]. Scientific Journal of Warsaw University of Life Science Economics and Organization of Logistics, 2020, 5(1): 65-78.
2	Caffrey Matthew B Jr. On Wargaming: How Wargames Have Shaped History and How They May Shape the Future[M]. Newport: Naval War College Press, 2019.
3	田忠良, 刘昊. 智能算法在兵棋对抗推演中的应用[J]. 指挥控制与仿真, 2021, 43(1): 40-47.
	Tian Zhongliang, Liu Hao. Application of Intelligent Algorithm in Military Chess Antagonism Deduction[J]. Command Control & Simulation, 2021, 43(1): 40-47.
4	刘满, 张宏军, 徐有为, 等. 群队级兵棋实体智能行为决策方法研究[J]. 系统工程与电子技术, 2022, 44(8): 2562-2569.
	Liu Man, Zhang Hongjun, Xu Youwei, et al. Research on Behavior Decision-making of Multi Entities in Group-level Wargame[J]. Systems Engineering and Electronics, 2022, 44(8): 2562-2569.
5	Colledanchise Michele, Ögren Petter. Behavior Trees in Robotics and AI: An Introduction[M]. Boca Raton: CRC Press, 2018.
6	田佩, 臧兆祥, 郭鸿村, 等. 海空兵棋中基于行为树的智能决策方法[J]. 长江信息通信, 2022, 35(4): 122-126.
	Tian Pei, Zang Zhaoxiang, Guo Hongcun, et al. Intelligent Decision-making Method Based on Behavior Tree in Sea-air Wargames[J]. Changjiang Information & Communications, 2022, 35(4): 122-126.
7	Afghah F, Zaeri-Amirani M, Razi A, et al. A Coalition Formation Approach to Coordinated Task Allocation in Heterogeneous UAV Networks[C]//2018 Annual American Control Conference (ACC). Piscataway: IEEE, 2018: 5968-5975.
8	Igarashi A, Elkind E. Hedonic Games with Graph-restricted Communication[C]//Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems. Richland: International Foundation for Autonomous Agents and Multiagent Systems, 2016: 242-250.
9	李琛, 黄炎焱, 张永亮, 等. Actor-Critic框架下的多智能体决策方法及其在兵棋上的应用[J]. 系统工程与电子技术, 2021, 43(3): 755-762.
	Li Chen, Huang Yanyan, Zhang Yongliang, et al. Multi-agent Decision-making Method Based on Actor-critic Framework and Its Application in Wargame[J]. Systems Engineering and Electronics, 2021, 43(3): 755-762.
10	陈晓轩, 冯旸赫, 黄金才, 等. 基于兵棋推演的空战编组对抗智能决策方法[J]. 指挥与控制学报, 2024, 10(2): 213-219.
	Chen Xiaoxuan, Feng Yanghe, Huang Jincai, et al. Intelligent Decision of Air Combat Formation Confrontation Based on War Game[J]. Journal of Command and Control, 2024, 10(2): 213-219.
11	Janovsky P, Deloach S A. Multi-agent Simulation Framework for Large-scale Coalition Formation[C]//2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI). Piscataway: IEEE, 2016: 343-350.
12	唐嘉钰, 李相民, 代进进, 等. 复杂约束条件下异构多智能体联盟任务分配[J]. 控制理论与应用, 2020, 37(11): 2413-2422.
	Tang Jiayu, Li Xiangmin, Dai Jinjin, et al. Coalition Task Allocation of Heterogeneous Multiple Agents with Complex Constraints[J]. Control Theory & Applications, 2020, 37(11): 2413-2422.
13	Choi H L, Brunet L, How J P. Consensus-based Decentralized Auctions for Robust Task Allocation[J]. IEEE Transactions on Robotics, 2009, 25(4): 912-926.
14	Schrittwieser J, Antonoglou I, Hubert T, et al. Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model[J]. Nature, 2020, 588(7839): 604-609.
15	张驭龙, 范长俊, 冯旸赫, 等. 任务级兵棋智能决策技术框架设计与关键问题分析[J]. 指挥与控制学报, 2024, 10(1): 19-25.
	Zhang Yulong, Fan Changjun, Feng Yanghe, et al. Technical Framework Design and Key Issues Analysis in Task-level Wargame Intelligent Decision Making[J]. Journal of Command and Control, 2024, 10(1): 19-25.

阵营	棋子类型	车(班)数	分值	初始位置
红	重型坦克	3	30	2 041
红	重型战车	3	21	2 041
红	无人战车	3	20	2 041
红	步兵	3	12	3 144
红	无人机	1	1	1 851
红	炮兵	3	0	1 547
红	巡飞弹	4	0	2 041
蓝	重型坦克	3	30	3 516
蓝	重型战车	3	21	3 416
蓝	无人战车	3	15	3 315
蓝	步兵	3	12	3 315
蓝	炮兵	3	0	4 508
蓝	无人机	1	1	4 318

[1]	Zhang Lun, Yang Mei, Zhao Tuo, Zhang Shuiku, Huang Jian. Task Reallocation Method for Unmanned Swarm Under Adversarial Conditions [J]. Journal of System Simulation, 2025, 37(1): 1-12.
[2]	Xiao Peng, Xie Feng, Ni Haihong, Zhang Min, Tang Zhili, Li Ni. Research on Collaborative Optimization Method of Multi-UAV Task Allocation and Path Planning [J]. Journal of System Simulation, 2024, 36(5): 1141-1151.
[3]	Shuangcheng Niu, Yuqiang Jin, Kunhu Kou. Research on Collaborative Task Allocation Method of Multiple UAVs Based on Blockchain [J]. Journal of System Simulation, 2023, 35(5): 949-956.
[4]	Kaituan Feng, Jie Yuan. Research on Discrete Workshop Task Assignment Based on Improved Water Filling Algorithm [J]. Journal of System Simulation, 2022, 34(4): 768-776.
[5]	Qi Xiaolong, Yang Xuguang. Time-varying Output Formation Tracking Control of Discrete-time Heterogeneous Multi-agent Systems [J]. Journal of System Simulation, 2022, 34(1): 36-44.
[6]	Wei Ruixuan, Wu Zichen. Study on Task Allocation of UAV Swarm Based on Cognitive Control [J]. Journal of System Simulation, 2021, 33(7): 1574-1581.
[7]	Tan Yaxin, Luo Jianhua, Fan Rui, Dong Zhiming. A Parallel Simulation Method for Combat organization and Implementation [J]. Journal of System Simulation, 2019, 31(11): 2350-2355.
[8]	Wang Rugen, Li Weimin, Liu Yonglan, Liu Jinsong. Research on Task Allocation Method of Unmanned Aerial Vehicle Cluster Network [J]. Journal of System Simulation, 2018, 30(12): 4794-4802.
[9]	Liang Hongtao, Kang Fengju. Task Allocation Modeling for Agent-oriented UUV Collaborative System [J]. Journal of System Simulation, 2015, 27(9): 2075-2082.