Diploma thesis Implementation of Backgammon player with neural network describes implementation of how is a computer capable to learn the game of backgammon. It is achieved with computer playing against itself, without any human help and without any prior knowledge about the game. Evaluation function of game positions, according to the thrown dice and checker positions, was implemented with neural network. Neural network is presented as a 2-layer perceptron.ududThe learning of neural network was achieved with reinforcement learning and backpropagation algorithm. Within backpropagation algorithm neural network plays several milions of backgammon games to achieve the advanced level of the game.ududTesting was also performed without any human interaction. Neural network played backgammon game against so called pubeval player, who also uses for its logic evaluation function to play on strong intermediate level. The testing showed that the neural network successfully accomplished all basic and also many of advanced features of game playing elements.ududDiploma work also involves realization of graphical user interface, which allows user to play backgammon against neural network.ud
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机译：文凭论文用神经网络实现西洋双陆棋玩家的实现，描述了如何学习计算机来学习西洋双陆棋游戏。它是通过计算机自力更生实现的，无需任何人工帮助，也无需任何游戏先验知识。利用掷骰子和棋子的位置，利用神经网络实现游戏位置的评估功能。神经网络以2层感知器的形式呈现。 ud ud通过增强学习和反向传播算法实现了神经网络的学习。在反向传播算法中，神经网络玩了数百万种西洋双陆棋游戏，以达到游戏的高级水平。 ud ud也进行了测试，无需任何人工干预。神经网络与所谓的pubeval玩家玩西洋双陆棋游戏，后者也利用其逻辑评估功能在强壮的中级水平上玩。测试表明，神经网络成功地完成了游戏元素的所有基本功能和许多高级功能。 ud ud文凭工作还涉及图形用户界面的实现，从而使用户可以在神经网络上玩西洋双陆棋。
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