{"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}
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.
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.