{"title":"Resolving the Resource Decision-Making Dilemma of Leaderless Group-Based Multiagent Systems and Repeated Games","authors":"Junxiao Xue;Mingchuang Zhang;Bowei Dong;Lei Shi;Andrés Adolfo Navarro Newball","doi":"10.1109/TSMC.2024.3427688","DOIUrl":null,"url":null,"abstract":"Leaderless rational individuals often lead the group into a resource decision dilemma in resource competition. Reducing the cost of resource competition while avoiding group decision dilemmas is a challenging task. Inspired by multiagent systems (MASs) and repeated games, we propose a decision-making reward discrimination (DRD) framework to address the resource competition dilemma of leaderless group formation. We aim to model the leaderless group’s resource gaming process using MAS and achieve optimal rewards for the group while minimizing conflict in resource competition. The proposed framework consists of three modules: 1) the decision-making module; 2) the reward module; and 3) the discriminative module. The decision-making module defines the agents and models the decision-making process, while the reward module calculates the group reward in each round using the reward matrix. The discriminative module compares the group reward with the target reward while providing the agent with environmental information. We verify the feasibility of the model through numerous experiments. The results show that agents adopt a revenge strategy to avoid resource competition dilemmas and achieve group reward optimality.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6000,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Systems Man Cybernetics-Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10611745/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Leaderless rational individuals often lead the group into a resource decision dilemma in resource competition. Reducing the cost of resource competition while avoiding group decision dilemmas is a challenging task. Inspired by multiagent systems (MASs) and repeated games, we propose a decision-making reward discrimination (DRD) framework to address the resource competition dilemma of leaderless group formation. We aim to model the leaderless group’s resource gaming process using MAS and achieve optimal rewards for the group while minimizing conflict in resource competition. The proposed framework consists of three modules: 1) the decision-making module; 2) the reward module; and 3) the discriminative module. The decision-making module defines the agents and models the decision-making process, while the reward module calculates the group reward in each round using the reward matrix. The discriminative module compares the group reward with the target reward while providing the agent with environmental information. We verify the feasibility of the model through numerous experiments. The results show that agents adopt a revenge strategy to avoid resource competition dilemmas and achieve group reward optimality.
在资源竞争中,无领导的理性个体往往会导致群体陷入资源决策困境。在避免群体决策困境的同时降低资源竞争成本是一项具有挑战性的任务。受多代理系统(MAS)和重复博弈的启发,我们提出了一种决策奖赏判别(DRD)框架,以解决无领导小组形成过程中的资源竞争困境。我们的目标是利用 MAS 对无领导小组的资源博弈过程进行建模,并在资源竞争冲突最小化的同时实现小组的最优回报。所提出的框架由三个模块组成:1)决策模块;2)奖励模块;3)判别模块。决策模块定义了代理并模拟了决策过程,而奖励模块则利用奖励矩阵计算每轮的群体奖励。判别模块在为代理提供环境信息的同时,将群体奖励与目标奖励进行比较。我们通过大量实验验证了该模型的可行性。结果表明,代理采用复仇策略避免了资源竞争困境,并实现了群体奖励最优。
期刊介绍:
The IEEE Transactions on Systems, Man, and Cybernetics: Systems encompasses the fields of systems engineering, covering issue formulation, analysis, and modeling throughout the systems engineering lifecycle phases. It addresses decision-making, issue interpretation, systems management, processes, and various methods such as optimization, modeling, and simulation in the development and deployment of large systems.