Deep neural network method to study computer game

Zhaoyong Deng, Weike Chen
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Abstract

Computer game is the main research direction in the field of artificial intelligence, the research object of this computer gameselection for card games of incomplete information game dou landlord, aiming at the problem of traditional modeling methods need to manually extract the features of, through the analysis of the principle of rival modeling algorithm and characteristics, this paper proposes a neural network and decision tree based on depth assessment modeling method of rivals, The tedious manual feature extraction process is avoided. The experimental results show that the overall winning chip is higher than that of the agents using static evaluation method, which improves the level of the system game to a certain extent.
用深度神经网络方法研究电脑游戏
计算机游戏是人工智能领域的主要研究方向,本次计算机游戏的研究对象为不完全信息纸牌游戏斗地主,针对传统建模方法需要人工提取特征的问题,通过分析对手建模算法的原理和特点,本文提出了一种基于神经网络和决策树深度评估的对手建模方法。避免了繁琐的手动特征提取过程。实验结果表明,采用静态评价方法的智能体总体获胜筹码高于静态评价方法的智能体,在一定程度上提高了系统博弈水平。
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