Article(id=1242140399033201162, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242140382134350274, articleNumber=null, orderNo=11, doi=10.3981/j.issn.1000-7857.2023.07.009, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1659456000000, receivedDateStr=2022-08-03, revisedDate=1667404800000, revisedDateStr=2022-11-03, acceptedDate=null, acceptedDateStr=null, onlineDate=1682587642177, onlineDateStr=2023-04-27, pubDate=1681315200000, pubDateStr=2023-04-13, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1682587642177, onlineIssueDateStr=2023-04-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774078948978, creator=sys-migrate, updateTime=1774078948978, updator=sys-migrate, issue=Issue{id=1242140382134350274, tenantId=1146029695717560320, journalId=1146031591421210625, year='2023', volume='41', issue='7', pageStart='1', pageEnd='127', issueExtLink='null', onlineDate='null', pubDate='1681315200000', pubDateStr='2023-04-13', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774078944950, creator='sys-migrate', updateTime=1774078944950, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=79, endPage=97, ext={EN=ArticleExt(id=1242140405282709571, articleId=1242140399033201162, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Principle and methodology of AlphaGo family algorithms, columnId=1242116810380743325, journalTitle=Science & Technology Review, columnName=Reviews, runingTitle=null, highlight=null, articleAbstract=AlphaGo family algorithms are important milestones in the history of artificial intelligence. These algorithms not only solve the typical complete information game problem such as Go but also are applicable to a wider range of problems. According to their development, this paper summarizes the fundamental principle and technical characteristics for the series of algorithms from AlphaGo Fan to MuZero, elaborating how the AlphaGo family algorithms work. The key technologies employed, including the Monte Carlo tree search, modeling and training of deep neural networks, are surveyed and compared. The AlphaGo family algorithms are of significant instructive value for addressing various problems in practice, from algorithm design, neural network modeling to model utilization. This paper helps to quickly understand the principle of these algorithms and is expected to provide useful reference for the further research and development of algorithms., authors=ZHANG Sheng¹, LONG Qiang^2*, KONG Yinan³, WANG Yu², authorsList=ZHANG Sheng, LONG Qiang, KONG Yinan, WANG Yu, authorCompany=1. Aerospace Technology Institute, China Aerodynamic Research and Development Center, Mianyang 621000, China
2. School of Mathematics and Physics, Southwest University of Science and Technology, Mianyang 621000, China
3. Computational Aerodynamics Institute, China Aerodynamic Research and Development Center, Mianyang 621000, China, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=5INZFhwGMIrJt51OLt0Khg==, pdfFileSize=4620804, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1242140403701456951, articleId=1242140399033201162, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=围棋人工智能AlphaGo系列算法的原理与方法, columnId=1242134377686180284, journalTitle=科技导报, columnName=综述, runingTitle=null, highlight=null, articleAbstract=围棋人工智能AlphaGo系列算法是人工智能发展历史中的重要里程碑事件。它们不仅成功地求解了以围棋为代表的完全信息博弈问题，而且具有更加广泛的适用性。依算法的发展历程，从基本原理与技术特征方面对 AlphaGo Fan 到 MuZero 的一系列算法进行了梳理，说明了AlphaGo系列算法的落子原理，阐释与对比了其中采用的关键技术：蒙特卡洛树搜索和深度神经网络的建模及训练。AlphaGo系列算法对解决实践中的其他重要问题，从算法设计、神经网络建模到模型利用等方面都具有重要的参考意义，本文的总结有助于快速地掌握这些算法的基本原理，从而为相关算法的研究与拓展提供有益参考。, authors=章胜¹，龙强^2*，孔轶男³，王宇², authorsList=章胜，龙强，孔轶男，王宇, authorCompany=1. 中国空气动力研究与发展中心空天技术研究所，绵阳 621000
2. 西南科技大学数理学院，绵阳 621000
3. 中国空气动力研究与发展中心计算空气动力研究所，绵阳 621000, correspAuthors=null, authorNote=章胜，副研究员，研究方向为系统优化与人工智能，电子信箱：zszhangshengzs1@outlook.com, correspAuthorsNote=龙强（通信作者），副教授，研究方向为人工智能，电子信箱：Longqiang@163.com, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=M21sApEAuzy4h7rcVTW0kQ==, pdfFileSize=4620804, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=国家自然科学基金项目（11902332）)}, authors=[Author(id=1277396067499029104, tenantId=1146029695717560320, journalId=null, articleId=1242140399033201162, orderNo=null, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=null, email=null, emailSecond=null, emailThird=null, correspondingAuthor=null, authorType=null, ext={}, companyList=null)], 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