蒙特卡罗围棋的UCT改进与序列模拟

Yizao Wang, S. Gelly
{"title":"蒙特卡罗围棋的UCT改进与序列模拟","authors":"Yizao Wang, S. Gelly","doi":"10.1109/CIG.2007.368095","DOIUrl":null,"url":null,"abstract":"Algorithm UCB1 for multi-armed bandit problem has already been extended to algorithm UCT which works for minimax tree search. We have developed a Monte-Carlo program, MoGo, which is the first computer Go program using UCT. We explain our modification of UCT for Go application and also the sequence-like random simulation with patterns which has improved significantly the performance of MoGo. UCT combined with pruning techniques for large Go board is discussed, as well as parallelization of UCT. MoGo is now a top-level computer-Go program on 9 times 9 Go board","PeriodicalId":365269,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence and Games","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"140","resultStr":"{\"title\":\"Modifications of UCT and sequence-like simulations for Monte-Carlo Go\",\"authors\":\"Yizao Wang, S. Gelly\",\"doi\":\"10.1109/CIG.2007.368095\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Algorithm UCB1 for multi-armed bandit problem has already been extended to algorithm UCT which works for minimax tree search. We have developed a Monte-Carlo program, MoGo, which is the first computer Go program using UCT. We explain our modification of UCT for Go application and also the sequence-like random simulation with patterns which has improved significantly the performance of MoGo. UCT combined with pruning techniques for large Go board is discussed, as well as parallelization of UCT. MoGo is now a top-level computer-Go program on 9 times 9 Go board\",\"PeriodicalId\":365269,\"journal\":{\"name\":\"2007 IEEE Symposium on Computational Intelligence and Games\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"140\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE Symposium on Computational Intelligence and Games\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIG.2007.368095\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE Symposium on Computational Intelligence and Games","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIG.2007.368095","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 140

摘要

求解多臂盗匪问题的UCB1算法已经推广到求解极大极小树搜索的UCT算法。我们开发了一个蒙特卡罗程序,MoGo,这是第一个使用UCT的计算机围棋程序。我们解释了我们对围棋应用程序UCT的修改,以及具有模式的序列式随机模拟,这大大提高了MoGo的性能。讨论了大棋局下的UCT与剪枝技术的结合,以及UCT的并行化。MoGo现在是一个顶级的9乘9棋盘计算机围棋程序
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Modifications of UCT and sequence-like simulations for Monte-Carlo Go
Algorithm UCB1 for multi-armed bandit problem has already been extended to algorithm UCT which works for minimax tree search. We have developed a Monte-Carlo program, MoGo, which is the first computer Go program using UCT. We explain our modification of UCT for Go application and also the sequence-like random simulation with patterns which has improved significantly the performance of MoGo. UCT combined with pruning techniques for large Go board is discussed, as well as parallelization of UCT. MoGo is now a top-level computer-Go program on 9 times 9 Go board
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
自引率
0.00%
发文量
0
期刊最新文献
Hybrid Evolutionary Learning Approaches for The Virus Game Vidya: A God Game Based on Intelligent Agents Whose Actions are Devised Through Evolutionary Computation Evolving Pac-Man Players: Can We Learn from Raw Input? Tournament Particle Swarm Optimization EvoTanks: Co-Evolutionary Development of Game-Playing Agents
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1