论文标题
GO游戏的派生指标 - 内在的网络强度评估和作弊检测
Derived metrics for the game of Go -- intrinsic network strength assessment and cheat-detection
论文作者
论文摘要
超人AI引擎的广泛可用性正在改变我们玩古老的GO游戏的方式。在Alphago系列系列中开发的开源软件包将重点从生产强大的游戏实体转变为提供分析游戏的工具。在这里，我们描述了第二代引擎（例如〜分数估计，可变Komi）的创新方式的两种方法，可用于定义新指标，以帮助加深我们对游戏的理解。首先，我们研究搜索组件除了原始神经网络策略输出外还贡献了多少信息。这为神经网络提供了内在的强度测量。其次，我们根据分数估计值的差异来定义移动的效果。这提供了对玩家的细粒度，移动的性能评估。我们将其用于打击检测在线作弊的新挑战。
The widespread availability of superhuman AI engines is changing how we play the ancient game of Go. The open-source software packages developed after the AlphaGo series shifted focus from producing strong playing entities to providing tools for analyzing games. Here we describe two ways of how the innovations of the second generation engines (e.g.~score estimates, variable komi) can be used for defining new metrics that help deepen our understanding of the game. First, we study how much information the search component contributes in addition to the raw neural network policy output. This gives an intrinsic strength measurement for the neural network. Second, we define the effect of a move by the difference in score estimates. This gives a fine-grained, move-by-move performance evaluation of a player. We use this in combating the new challenge of detecting online cheating.
