基于线性规划的不完全信息游戏强化学习机制

IF 15.3 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Baosen Yang;Changbing Tang;Yang Liu;Guanghui Wen;Guanrong Chen
{"title":"基于线性规划的不完全信息游戏强化学习机制","authors":"Baosen Yang;Changbing Tang;Yang Liu;Guanghui Wen;Guanrong Chen","doi":"10.1109/JAS.2024.124464","DOIUrl":null,"url":null,"abstract":"Dear Editor, Recently, with the development of artificial intelligence, game intelligence decision-making has attracted more and more attention. In particular, incomplete-information games (IIG) have gradually become a new research focus, where players make decisions without sufficient information, such as the opponent's strategies or preferences. In this case, a selfish player can only make reactive decisions based on the changes in environment and state. Thus, blind decisions by players may drift them away from the path of reward maximization, and may even hinder the health of the IIG environment. Therefore, it is necessary to design an effective mechanism to optimize decision-making for IIG players.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 11","pages":"2340-2342"},"PeriodicalIF":15.3000,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10707689","citationCount":"0","resultStr":"{\"title\":\"A Linear Programming-Based Reinforcement Learning Mechanism for Incomplete-Information Games\",\"authors\":\"Baosen Yang;Changbing Tang;Yang Liu;Guanghui Wen;Guanrong Chen\",\"doi\":\"10.1109/JAS.2024.124464\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Dear Editor, Recently, with the development of artificial intelligence, game intelligence decision-making has attracted more and more attention. In particular, incomplete-information games (IIG) have gradually become a new research focus, where players make decisions without sufficient information, such as the opponent's strategies or preferences. In this case, a selfish player can only make reactive decisions based on the changes in environment and state. Thus, blind decisions by players may drift them away from the path of reward maximization, and may even hinder the health of the IIG environment. Therefore, it is necessary to design an effective mechanism to optimize decision-making for IIG players.\",\"PeriodicalId\":54230,\"journal\":{\"name\":\"Ieee-Caa Journal of Automatica Sinica\",\"volume\":\"11 11\",\"pages\":\"2340-2342\"},\"PeriodicalIF\":15.3000,\"publicationDate\":\"2024-10-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10707689\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ieee-Caa Journal of Automatica Sinica\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10707689/\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ieee-Caa Journal of Automatica Sinica","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10707689/","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

摘要

亲爱的编辑,近年来,随着人工智能的发展,博弈智能决策越来越受到人们的关注。其中,不完全信息博弈(incomplete-information game,IIG)逐渐成为一个新的研究热点。在这种情况下,自私的玩家只能根据环境和状态的变化做出被动决策。因此,棋手的盲目决策可能会使他们偏离回报最大化的道路,甚至会阻碍 IIG 环境的健康发展。因此,有必要设计一种有效的机制来优化 IIG 玩家的决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Linear Programming-Based Reinforcement Learning Mechanism for Incomplete-Information Games
Dear Editor, Recently, with the development of artificial intelligence, game intelligence decision-making has attracted more and more attention. In particular, incomplete-information games (IIG) have gradually become a new research focus, where players make decisions without sufficient information, such as the opponent's strategies or preferences. In this case, a selfish player can only make reactive decisions based on the changes in environment and state. Thus, blind decisions by players may drift them away from the path of reward maximization, and may even hinder the health of the IIG environment. Therefore, it is necessary to design an effective mechanism to optimize decision-making for IIG players.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Ieee-Caa Journal of Automatica Sinica
Ieee-Caa Journal of Automatica Sinica Engineering-Control and Systems Engineering
CiteScore
23.50
自引率
11.00%
发文量
880
期刊介绍: The IEEE/CAA Journal of Automatica Sinica is a reputable journal that publishes high-quality papers in English on original theoretical/experimental research and development in the field of automation. The journal covers a wide range of topics including automatic control, artificial intelligence and intelligent control, systems theory and engineering, pattern recognition and intelligent systems, automation engineering and applications, information processing and information systems, network-based automation, robotics, sensing and measurement, and navigation, guidance, and control. Additionally, the journal is abstracted/indexed in several prominent databases including SCIE (Science Citation Index Expanded), EI (Engineering Index), Inspec, Scopus, SCImago, DBLP, CNKI (China National Knowledge Infrastructure), CSCD (Chinese Science Citation Database), and IEEE Xplore.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信