解释赢得扑克——一种数据挖掘方法

U. Johansson, Cecilia Sönströd, L. Niklasson
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引用次数: 10

摘要

本文介绍了一个应用程序,其中机器学习技术用于挖掘从在线扑克收集的数据,以解释什么是成功的游戏。这项研究的重点是人手不足的小赌注德州扑克,使用的数据集包含105名人类玩家,每个人都玩过500多手。使用的技术是决策树和G-REX,一种基于遗传规划的规则提取器。总的结果是,归纳出的规则相当紧凑,具有很高的准确性,从而为成功的游戏提供了很好的解释。当然,很难评估这些规则的质量;也就是说,如果他们提供了一些新颖而不平凡的东西。然而,主要的情况是,获得的规则与建立的扑克理论是一致的。考虑到这一点,我们相信,在未来的研究中,当有更多的数据可用时,建议的技术将会对扑克输赢之间的差异产生清晰而准确的描述
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Explaining Winning Poker--A Data Mining Approach
This paper presents an application where machine learning techniques are used to mine data gathered from online poker in order to explain what signifies successful play. The study focuses on short-handed small stakes Texas Hold'em, and the data set used contains 105 human players, each having played more than 500 hands. Techniques used are decision trees and G-REX, a rule extractor based on genetic programming. The overall result is that the rules induced are rather compact and have very high accuracy, thus providing good explanations of successful play. It is of course quite hard to assess the quality of the rules; i.e. if they provide something novel and non-trivial. The main picture is, however, that obtained rules are consistent with established poker theory. With this in mind, we believe that the suggested techniques will in future studies, where substantially more data is available, produce clear and accurate descriptions of what constitutes the difference between winning and losing in poker
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