Modeling players' behaviors in games has gained increased momentum in the past few years. This area of research has wide applications, including modeling learners and understanding player strategies, to mention a few. In this paper, we present a new methodology, called Interactive Behavior Analytics (IBA), comprised of two visualization systems, a labeling mechanism, and abstraction algorithms that use Dynamic Time Warping and clustering algorithms. The methodology is packaged in a seamless interface to facilitate knowledge discovery from game data. We demonstrate the use of this methodology with data from two multiplayer team-based games: BoomTown, a game developed by Gallup, and DotA 2. The results of this work show the effectiveness of this method in modeling, and developing human-interpretable models of team and individual behavior.

中文翻译：

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通过时空分析为个人和团队行为建模

在过去几年中，在游戏中对玩家行为建模的势头越来越大。这一研究领域具有广泛的应用，包括建模学习者和理解玩家策略，仅举几例。在本文中，我们提出了一种称为交互式行为分析 (IBA) 的新方法，它由两个可视化系统、一个标记机制和使用动态时间扭曲和聚类算法的抽象算法组成。该方法被封装在一个无缝界面中，以促进从游戏数据中发现知识。我们使用来自两个多人团队游戏的数据演示了这种方法的使用：由盖洛普开发的游戏 BoomTown 和 DotA 2。这项工作的结果显示了这种方法在建模和开发人类可解释的模型方面的有效性团队和个人行为。