混合Gomoku深度学习人工智能

Peizhi Yan, Yi Feng
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引用次数: 7

摘要

围棋是一种古老的棋盘游戏。求解Gomoku的传统方法是对Gomoku博弈树进行树搜索。尽管《Gomoku》的规则很简单,但游戏树的复杂性却是巨大的。与象棋和幕府将军等其他棋盘游戏不同,Gomoku的棋盘状态更直观。这个功能类似于另一种著名的棋盘游戏——围棋。AlphaGo的成功[5,6]启发了我们将监督学习方法和深度神经网络应用于解决Gomoku游戏。我们设计了一个深度卷积神经网络模型来帮助机器从训练数据中学习。在我们的实验中,训练数据的准确率为69%,测试数据的准确率为38%。最后,我们将训练好的深度神经网络模型与硬编码的基于卷积的Gomoku评估函数相结合,形成了混合Gomoku人工智能(AI),进一步提高了性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Hybrid Gomoku Deep Learning Artificial Intelligence
Gomoku is an ancient board game. The traditional approach to solving the Gomoku is to apply tree search on a Gomoku game tree. Although the rules of Gomoku are straightforward, the game tree complexity is enormous. Unlike other board games such as chess and Shogun, the Gomoku board state is more intuitive. This feature is similar to another famous board game, the game of Go. The success of AlphaGo [5, 6] inspired us to apply a supervised learning method and deep neural network in solving the Gomoku game. We designed a deep convolutional neural network model to help the machine learn from the training data. In our experiment, we got 69% accuracy on the training data and 38% accuracy on the testing data. Finally, we combined the trained deep neural network model with a hard-coded convolution-based Gomoku evaluation function to form a hybrid Gomoku artificial intelligence (AI) which further improved performance.
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