Overcoming Alpha-Beta Limitations Using Evolved Artificial Neural Networks

Y. Gal, M. Avigal
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Abstract

In order to give the computer the ability to play against human opponents, one could utilize the Alpha-Beta algorithm. However, this algorithm has several limitations restricting its playing capabilities. Over the years, many variants of this algorithm were developed, among them a couple that make use of neural networks: a neural network to focus the search in the game tree, and a neural network trained without expert knowledge that substitutes the heuristic function in the Alpha-Beta algorithm. In this paper the weaknesses of the Alpha-Beta algorithm are reviewed alongside its variants that use neural networks. It is explained how each approach overcomes different limitations of the Alpha-Beta algorithm, and an attempt to overcome its weaknesses by the use of a combination of the neural network algorithms is presented. The proposed hybrid algorithm, which was developed using Evolutionary Strategies, still keeps the advantages of each of the individual neural algorithms, and shows a significant improvement in play against them.
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利用进化的人工神经网络克服Alpha-Beta限制
为了赋予计算机与人类对手对抗的能力,人们可以利用Alpha-Beta算法。然而,这种算法有一些限制,限制了它的播放能力。多年来,该算法的许多变体被开发出来,其中有几个使用了神经网络:一个神经网络专注于游戏树中的搜索,另一个神经网络在没有专家知识的情况下训练,替代了Alpha-Beta算法中的启发式函数。本文回顾了Alpha-Beta算法的弱点以及它使用神经网络的变体。它解释了每种方法如何克服Alpha-Beta算法的不同限制,并尝试通过使用神经网络算法的组合来克服其弱点。使用进化策略开发的混合算法仍然保留了每个单独神经算法的优点,并且在对抗它们时显示出显着的改进。
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