A Decision-making Method for Swarm Agents in Attack-defense Confrontation

被引:2
|
作者
Wang, Lexing [1 ,2 ]
Qiu, Tenghai [1 ]
Pu, Zhiqiang [1 ,2 ]
Yi, Jianqiang [1 ,2 ]
Zhu, Jinying [1 ]
Zhao, Yanjie [3 ]
机构
[1] Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[3] China Acad Elect & Informat Technol, Beijing 100041, Peoples R China
来源
IFAC PAPERSONLINE | 2023年 / 56卷 / 02期
基金
中国国家自然科学基金;
关键词
Coalition formation; target allocation; decision-making;
D O I
10.1016/j.ifacol.2023.10.020
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The cooperative decision-making of swarm agents has attracted extensive attention from researchers due to its potential applications in multidisciplinary engineering problems. This paper studies a confrontation problem called asymmetric attack-defense confrontation (i.e., considering the difference in capability and quantity between agents and targets). The objective is to develop an effective self-organized swarm confrontation decision-making method. The decision-making process consists of task allocation decision and swarm motion decision. At each decision-making step, firstly, each agent forms a coalition with other agents autonomously by using a proposed hedonic coalition formation algorithm according to the attribute of targets. Thus, the agents assigned to the same target form a coalition, and swarm agents form several disjoint coalitions. Then, based on the allocated results, the agents are steered toward the corresponding target by a combat stimulus and a proposed selected interaction swarm algorithm. Finally, while the targets are within the agents' attack radius, the agents execute the confrontation decision. Simulation results show the effectiveness of the designed method. Copyright (c) 2023 The Authors.
引用
收藏
页码:7858 / 7864
页数:7
相关论文
共 50 条
  • [1] A Bio-Inspired Decision-Making Method of UAV Swarm for Attack-Defense Confrontation via Multi-Agent Reinforcement Learning
    Chi, Pei
    Wei, Jiahong
    Wu, Kun
    Di, Bin
    Wang, Yingxun
    BIOMIMETICS, 2023, 8 (02)
  • [2] Deep reinforcement learning based multi-AUVs cooperative decision-making for attack-defense confrontation missions
    Xu, Jian
    Huang, Fei
    Wu, Di
    Cui, Yunfei
    Yan, Zheping
    Zhang, Kai
    OCEAN ENGINEERING, 2021, 239
  • [3] Adaptive Decision-Making in Attack-Defense Games With Bayesian Inference of Rationality Level
    Fang, Hongwei
    Yi, Peng
    IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2024, 71 (12) : 16558 - 16567
  • [4] A method of network attack-defense game and collaborative defense decision-making based on hierarchical multi-agent reinforcement learning
    Tang, Yunlong
    Sun, Jing
    Wang, Huan
    Deng, Junyi
    Tong, Liang
    Xu, Wenhong
    COMPUTERS & SECURITY, 2024, 142
  • [5] Security Analysis for CBTC Systems under Attack-Defense Confrontation
    Wu, Wenhao
    Bu, Bing
    ELECTRONICS, 2019, 8 (09)
  • [6] Research on the game of network security attack-defense confrontation through the optimal defense strategy
    Liu, Fei
    Gao, Hongyan
    Wei, Zegang
    SECURITY AND PRIVACY, 2021, 4 (01)
  • [7] Offense-defense confrontation decision making for dynamic UAV swarm versus UAV swarm
    Xing, Dongjing
    Zhen, Ziyang
    Gong, Huajun
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING, 2019, 233 (15) : 5689 - 5702
  • [8] Adaptive attack occupancy maneuver decision of UUV attack-defense game
    Wang, Zhao
    Wang, Hong-Jian
    Zhang, Hong-Han
    Yu, Dan
    Ren, Jing-Fei
    Kongzhi yu Juece/Control and Decision, 2024, 39 (11): : 3819 - 3828
  • [9] Collaborative decision-making for UAV swarm confrontation based on reinforcement learning
    Jiao, Yongkang
    Fu, Wenxing
    Cao, Xinying
    Su, Qiangqing
    Wang, Yusheng
    Shen, Zixiang
    Yu, Lanlin
    IET CONTROL THEORY AND APPLICATIONS, 2025, 19 (01):
  • [10] Malware propagation model in wireless sensor networks under attack-defense confrontation
    Zhou, Haiping
    Shen, Shigen
    Liu, Jianhua
    COMPUTER COMMUNICATIONS, 2020, 162 (162) : 51 - 58