文献类型：期刊文献

英文题名：Matching-Based Capture-the-Flag Games for Multiagent Systems

作者：Wang, Jiali[1];Zhou, Zhao[1];Jin, Xin[2];Mao, Shuai[3];Tang, Yang[1,4]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Fudan Univ, Res Inst Intelligent Complex Syst, Shanghai 200433, Peoples R China;[3]Nantong Univ, Dept Elect Engn, Nantong 226019, Peoples R China;[4]East China Univ Sci & Technol, Engn Res Ctr Proc Syst Engn, Minist Educ, Shanghai 200237, Peoples R China

年份：2024

卷号：16

期号：3

起止页码：993

外文期刊名：IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS

收录：;EI(收录号：20234314970295);WOS:【SCI-EXPANDED(收录号:WOS:001247154200023)】；

基金：No Statement Available

语种：英文

外文关键词：Games; Differential games; Task analysis; Mathematical models; Heuristic algorithms; Computational complexity; Capture-the-flag differential games; cognitive learning; multiagent systems; optimal strategy; task assignment

摘要：Competition and cooperation among agents in multiagent systems can be effectively modeled as differential games. One of the typical tasks is capturing the flag, in which the agents can be divided into attackers' alliance and defenders' alliance with two-phase competitive behaviors. The attackers' alliance aims to capture flags in the first phase and then return to the safe region in the second phase, while the defenders' alliance aims to protect the flags and apprehend as many attackers as possible. Throughout the interaction, agents are actively involved in perception, cognitive learning, and the formulation of optimal decisions rooted in their acquired knowledge. Consequently, this article delves into the central challenges posed by capture-the-flag differential games, particularly in terms of task allocation and coordinated apprehension strategies among defenders. First, we use the Apollonius circle to transform the multiplayer capture-the-flag problems into one-defense-one or two-defense-one scenarios. By analyzing the advantages of cooperation between defenders, a two-stage joint optimal strategy is obtained. Moreover, we propose an approximation algorithm that achieves optimal task assignment, significantly reducing computational complexity. The performance and effectiveness of the proposed algorithm are demonstrated through numerical simulations.