[1]王亚杰,乔继林,梁凯,等.结合先验知识与蒙特卡罗模拟的麻将博弈研究[J].智能系统学报,2022,17(1):69-78.[doi:10.11992/tis.20210730]
 WANG Yajie,QIAO Jilin,LIANG Kai,et al.Research on mahjong game based on prior knowledge and Monte Carlo simulation[J].CAAI Transactions on Intelligent Systems,2022,17(1):69-78.[doi:10.11992/tis.20210730]
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结合先验知识与蒙特卡罗模拟的麻将博弈研究

参考文献/References:
[1] 王骄, 徐心和. 计算机博弈: 人工智能的前沿领域: 全国大学生计算机博弈大赛[J]. 计算机教育, 2012(7): 14–18
WANG Jiao, XU Xinhe. Computer game: the frontier field of artificial intelligence: the national college student computer game competition[J]. Computer education, 2012(7): 14–18
[2] 王亚杰, 邱虹坤, 吴燕燕, 等. 计算机博弈的研究与发展[J]. 智能系统学报, 2016, 11(6): 788–798
WANG Yajie, QIU Hongkun, WU Yanyan, et al. Research and development of computer games[J]. CAAI transactions on intelligent systems, 2016, 11(6): 788–798
[3] 徐心和, 邓志立, 王骄, 等. 机器博弈研究面临的各种挑战[J]. 智能系统学报, 2008, 3(4): 287–293
XU Xinhe, DENG Zhili, WANG Jiao, et al. Challenging issues facing computer game research[J]. CAAI transactions on intelligent systems, 2008, 3(4): 287–293
[4] SILVER D, HUANG A, MADDISON C J, et al. Mastering the game of Go with deep neural networks and tree search[J]. Nature, 2016, 529(7587): 484–489.
[5] SILVER D, SCHRITTWIESER J, SIMONYAN K, et al. Mastering the game of Go without human knowledge[J]. Nature, 2017, 550(7676): 354–359.
[6] SILVER D, HUBER T, SCHRITTWIESER J, et al. Mastering chess and shogi by self-play with a general reinforcement learning algorithm[J]. IEEE transactions on computational intelligence and AI in games, 2017, 3(2): 167–170.
[7] SILVER D, HUBERT T, SCHRITTWIESER J, et al. A general reinforcement learning algorithm that masters chess, shogi, and go through self-play[J]. Science, 2018, 362(6419): 1140–1144.
[8] 李翔, 姜晓红, 陈英芝, 等. 基于手牌预测的多人无限注德州扑克博弈方法[J]. 计算机学报, 2018, 41(1): 47–64
LI Xiang, JIANG Xiaohong, CHEN Yingzhi, et al. Game in multiplayer no-limit texas Hold’Em based on hands prediction[J]. Chinese journal of computers, 2018, 41(1): 47–64
[9] MORAV?íK M, SCHMID M, BURCH N, et al. DeepStack: expert-level artificial intelligence in heads-up no-limit poker[J]. Science, 2017, 356(6337): 508–513.
[10] BROWN N, SANDHOLM T. Superhuman AI for multiplayer poker[J]. Science, 2019, 365(6456): 885–890.
[11] NOAM B. Equilibrium finding for large adversarial imperfect-information Games[D]. Pittsburgh: Carnegie Mellon University, 2020.
[12] 微软亚洲研究院.哪类游戏AI难度更高?用数学来分析一下[EB/OL]. (2019-8-16) [2021-07-17]. https://www.msra.cn/zh-cn/news/features/difficulty-of-ai-games.
MSRA. Which game is more difficult for AI? Use math to analyze[EB/OL]. (2019-8-16) [2021-07-17]. https://www.msra.cn/zh-cn/news/features/difficulty-of-ai- games.
[13] BROWN N, SANDHOLM T. Safe and nested subgame solving for imperfect-information games[J]. NIPS, 2017: 690–700.
[14] XINGDREAM. 2020麻将项目比赛规则和本文完整实验源码[EB/OL]. (2021-06-01) [2021-07-17]. https://github.com/xingdream/mahjong.
XINGDREAM. 2020 Mahjong competition rules and the complete experimental source code of this article[EB/OL]. (2021-06-01) [2021-07-17]. https://github.com/xingdream/mahjong.
[15] CHENG Yuan, LI Chikwong, LI Sharon H. Mathematical aspect of the combinatorial game “Mahjong”[J]. Southeast asian bulletin of Mathematics, 2019, 43: 815–826.
[16] LI Sanjiang, YAN Xueqing. Let’s play mahjong[J]. IEICE transactions on fundamentals of electronics, communications and computer sciences, 2019, abs/1903.03294.
[17] 林典馀, 吴毅成. 麻将之人工智慧研究[D]. 新竹: 国立交通大学,2008.
LIN Dianyu, WU Yicheng. The study of mahjong artificial intelligence[D]. Xinzhu: National Chiao Tung University, 2008.
[18] 陈新飏, 林顺喜. 电脑麻将程序ThousandWind 的设计与实作[D]. 新竹: 国立台湾师范大学, 2013.
CHEN Xinsi, LIN Shunxi. The design and implementation of the mahjong program ThousandWind[D]. Xinzhu: National Taiwan Normal University, 2013.
[19] 曾海洋, 颜士净. 蒙特卡罗麻将程式设计与改良[D]. 新竹: 台湾计算机博弈学会, 2015.
ZENG Haiyang, YAN Shijing. Monte Carlo Mahjong programming and improvement[D]. Xinzhu: Taiwan Computer Game Association, 2015.
[20] HANDA H. Evolution of the weight vectors in Mahjong non-player characters[C]//2013 World Congress on Nature and Biologically Inspired Computing. New York, USA: IEEE, 2013: 147?152.
[21] MIZUKAMI N, TSURUOKA Y. Building a computer Mahjong player based on Monte Carlo simulation and opponent models[C]//2015 IEEE Conference on Computational Intelligence and Games. New York, USA: IEEE, 2015: 275?283.
[22] GAO Shiqi, OKUYA Fuminori, KAWAHARA Yoshihiro, et al. Supervised learning of imperfect information data in the game of mahjong via deep convolutional neural networks[J]. Information processing society of Japan, 2018(2018): 43–50.
[23] GAO SHIQI, OKUYA F, KAWAHARA Y, et al. Building a computer mahjong player via deep convolutional neural networks[EB/OL]. (2019-06-00) [2021-07-17]. https://arxiv.org/abs/1906.02146.
[24] LI JUNJIE, KOYAMADA S, YE QIWEI, et al. Suphx: mastering mahjong with deep reinforcement learning[EB/OL]. (2020-03-30) [2021-07-17]. https://arXiv preprint arXiv:2003.13590.
[25] WANG Mingyan, YAN Tianwei, LUO Mingyuan, et al. A novel deep residual network-based incomplete information competition strategy for four-players Mahjong games[J]. Multimedia tools and applications, 2019, 78(16): 23443–23467.
[26] 任航. 基于知识与树搜索的非完备信息博弈决策的研究与应用[D]. 南昌: 南昌大学, 2020.
REN Hang. Research and application of imperfect information game decision based on knowledge and game-tree search[D]. Nanchang: Nanchang University, 2020.
[27] 雷捷维, 王嘉旸, 任航, 等. 基于Expectimax搜索与Double DQN的非完备信息博弈算法[J]. 计算机工程, 2021, 47(3): 304, 310–320
LEI Jiewei, WANG Jiayang, REN Hang, et al. Incomplete information game algorithm based on expectimax search and double DQN[J]. Computer engineering, 2021, 47(3): 304, 310–320
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备注/Memo

收稿日期:2021-07-17。
基金项目:辽宁省兴辽英才计划项目(XLYC1906003).
作者简介:王亚杰,教授,博士,中国人工智能学会理事,中国人工智能学会机器博弈专业委员会副主任,主要研究方向为机器博弈、模式识别、图像融合。主持和参与课题20余项。发表学术论文60余篇;乔继林,硕士研究生,主要研究方向为机器博弈。在2020年计算机博弈大赛麻将项目中获得冠军;梁凯,硕士研究生,主要研究方向为机器博弈。参与2020年计算机博弈大赛项目并获得冠军。
通讯作者:王亚杰. E-mail: wangyajie@sina.com

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