[1]高强,徐心和,王昊,等.一种基于经验的德州扑克博弈系统架构[J].智能系统学报,2020,15(3):468-474.[doi:10.11992/tis.201803043]
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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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]
一种基于经验的德州扑克博弈系统架构
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
15
期数:
2020年第3期
页码:
468-474
栏目:
学术论文—智能系统
出版日期:
2020-05-05
- Title:
- System architecture of Texas Hold’em based on experience
- 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
- 分类号:
- TP301.5
- 摘要:
- 为了利用历史经验知识提高德州扑克博弈水平，提出一种二人赌注无上限的德州扑克博弈系统架构：对于知识库模块，利用海量历史牌局训练得到基于CNN的深度学习网络模型并构建了一个专家经验库；在系统的搜索模块中，构建了一种分阶段的德州扑克博弈树，利用专家经验和历史经验引导德州扑克博弈树的展开；对于系统的估值核心模块，构建了一种基于哈希技术的牌型对照表，以提高系统判定胜负的效率。实验结果表明本文提出的博弈系统架构具有更高的对弈水平。
- 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.
- 参考文献/References:
-
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备注/Memo
收稿日期:2018-03-26。
基金项目:辽宁省自然科学基金项目(20180550146，20170520386)
作者简介:高强，讲师，博士，主要研究方向为机器博弈、计算复杂性理论;徐心和，教授，博士生导师，中国人工智能学会常务理事，主要研究方向为控制理论与应用、系统仿真、智能机器人、机器博弈。主持完成国家自然科学基金、863基金、国家“八五”、“九五”攻关课题13项，其中8项通过省、部级鉴定，获科技进步奖国家三等1项，省部级科技进步奖多项。发表学术论文300余篇;王昊，博士研究生，主要研究方向为机器博弈
通讯作者:高强.E-mail:tommy_06@163.com
更新日期/Last Update:
1900-01-01