[1]WU Licheng,WU Qifei,ZHONG Hongming,et al.Algorithm for “Hearts” game based on convolutional neural network[J].CAAI Transactions on Intelligent Systems,2023,18(4):775-782.[doi:10.11992/tis.202203030]
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CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
18
Number of periods:
2023 4
Page number:
775-782
Column:
学术论文—智能系统
Public date:
2023-07-15
- Title:
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Algorithm for “Hearts” game based on convolutional neural network
- Author(s):
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WU Licheng; WU Qifei; ZHONG Hongming; WANG Shiyao; LI Xiali
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School of Information Engineering, Minzu University of China, Beijing 100081, China
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- Keywords:
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artificial intelligence; game of incomplete information; deep learning; convolutional neural network; Hearts; Chinese card game; card-showing; card-playing
- CLC:
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TP183;G892
- DOI:
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10.11992/tis.202203030
- Abstract:
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“Hearts”, also known as “Chinese card game”, is a very characteristic poker game, which belongs to incomplete information games. It consists of two stages of card showdown and card playing, and there is strong reversality throughout the game. In order to study the computer game algorithm of “Hearts”, this paper proposes a “Hearts” game algorithm based on deep learning, which includes two neural networks, namely, card showdown and card playing, which are used in card showdown and card playing stage respectively. Both the card showdown network and card playing network are constructed by convolutional neural network (CNN), which are designed into different network structures according to their functional characteristics. Two CNN networks are trained, tested, and analyzed by using the real card playing patterns of 11,000 human advanced players to generate training data and test data proportionally. The results show that the accuracy of card showdown and card playing network reaches 88.4% and 71.4% respectively. The analysis of some specific examples of card showdown and card playing shows that the algorithm is able to produce reasonable card showdown and card playing strategies.