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Only-One-Victor Pattern Learning in Computer Go

机译：计算机围棋中唯一的胜利者模式学习

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摘要

Automatically acquiring domain knowledge from professional game records, a kind of pattern learning, is an attractive and challenging issue in computer Go. This paper proposes a supervised learning method, by introducing a new generalized Bradley-Terry model, named Only-One-Victor, to learn patterns from game records. Basically, our algorithm applies the same idea with Elo rating algorithm, which considers each move in game records as a group of move patterns, and the selected move as the winner of a kind of competition among all groups on current board. However, being different from the generalized Bradley-Terry model for group competition used in Elo rating algorithm, Only-One-Victor model in our work simulates the process of making selection from a set of possible candidates by considering such process as a group of independent pairwise comparisons. We use a graph theory model to prove the correctness of Only-One-Victor model. In addition, we also apply the Minorization-Maximization (MM) to solve the optimization task. Therefore, our algorithm still enjoys many computational advantages of Elo rating algorithm, such as the scalability with high dimensional feature space. With the training set containing 115,832 moves and the same feature setting, the results of our experiments show that Only-One-Victor outperforms Elo rating, a well-known best supervised pattern learning method.
机译：从专业游戏记录中自动获取领域知识（一种模式学习）是计算机Go中一个有吸引力且具有挑战性的问题。本文通过引入一种新的广义Bradley-Terry模型（称为Only-One-Victor），提出了一种监督学习方法，以从游戏记录中学习模式。基本上，我们的算法采用与Elo评分算法相同的思想，该算法将游戏记录中的每个举动视为一组移动模式，并将选定的举动视为当前棋盘上所有组之间某种竞争的获胜者。但是，与Elo评分算法中用于小组竞赛的广义Bradley-Terry模型不同，我们的工作中Only-One-Victor模型通过将这样的过程视为一组独立的模型来模拟从一组可能的候选人中进行选择的过程成对比较。我们使用图论模型来证明Only-One-Victor模型的正确性。此外，我们还应用最小化最大化（MM）来解决优化任务。因此，我们的算法仍然享有Elo评级算法的许多计算优势，例如具有高维特征空间的可伸缩性。通过包含115,832个动作的训练集和相同的功能设置，我们的实验结果表明，Only-One-Victor胜过Elo评分，Elo评分是著名的最佳监督模式学习方法。

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