[1]GAO Qiang,XU Xinhe,WANG Hao,et al.System architecture of Texas Hold’em based on experience[J].CAAI Transactions on Intelligent Systems,2020,15(3):468-474.[doi:10.11992/tis.201803043]
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System architecture of Texas Hold’em based on experience

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 3 Page number: 468-474 Column: 学术论文—智能系统 Public date: 2020-05-05
Title:
System architecture of Texas Hold’em based on experience
Author(s):
GAO Qiang1;  XU Xinhe2;  WANG Hao3;  BAI Guoli3;  CAO Ruimin3
1. Key Laboratory of Manufacturing Industrial Integrated Automation, Shenyang University, Shenyang 110044, China;
2. College of Information Science and Engineering, Northeastern University, Shenyang 110819, China;
3. School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Keywords:
Heads-up no-limit Texas Hold’em; computer games; dynamic game with imperfect information; game tree; deep learning; expert database; Hash table; game strategy
CLC:
TP301.5
DOI:
10.11992/tis.201803043
Abstract:
To improve the level of Texas Hold’em through historical experience, this paper proposes a system architecture of heads-up no-limit Texas Hold’em for the knowledge base module. Mass historic games are used to train the deep learning network based on convolutional neural network, and an expert database is constructed for the search module of the system. Texas Hold’em structured game tree is developed and extended, and it is applied in terms of the expertise and historical experience to the core module for evaluation. A hand-ranking hash-based table is built to reduce the time required to evaluate hand rankings. The experimental result shows a higher playing level for the proposed system architecture.
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