[1]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]
Copy

Research on mahjong game based on prior knowledge and Monte Carlo simulation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 1 Page number: 69-78 Column: 学术论文—机器学习 Public date: 2022-01-05
Title:
Research on mahjong game based on prior knowledge and Monte Carlo simulation
Author(s):
WANG Yajie1;  QIAO Jilin2;  LIANG Kai2;  XIE Yanyan2
1. Engineering Training Center, Shenyang Aerospace University, Shenyang 110136, China;
2. School of Computer Science, Shenyang Aerospace University, Shenyang 110136, China
Keywords:
mahjong; game; prior knowledge; Monte Carlo; opponent’s hand; simulation; win by discard; win rate
CLC:
TP18
DOI:
10.11992/tis.20210730
Abstract:
In view of the difficulty in designing game algorithms based on supervised learning due to the shortage of a unified platform and a large amount of card score data for inland mahjong, ,this paper designs a series of game algorithms that combine rules, experience and the Monte Carlo method for inland mahjong game. Firstly, the fold priority, effective number of draws and the eating priority are proposed for the discard module, draw module, and card eating module of the mahjong game, respectively. The mahjong AI knowledge system is improved, and the basic game algorithm Fanfou_ba and the optimized game algorithm Fanfou_op are designed. Secondly, the game algorithm Fanfou_op is proposed that reduces the probability of firing a shot by using the Monte Carlo method to simulate the waiting opponent’s hand. Finally, comparative experiments are conducted on these three kinds of game algorithms. The experimental results show that compared with Fanfou_ba, the Fanfou_op algorithm improves the win rate by 9.76%, and that compared with the Fanfou_op algorithm, the Fanfou_mc algorithm enhances win rate by 0.13% and reduces the shot rate by 0.47%, which proves that the improvement strategy proposed is feasible and effective.
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
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems