Machine learning algorithms for automated analysis of player behavior in next-generation video games

与视频游戏制造商育碧(蒙特利尔)合作，我们将开发机器学习算法，以提高沉浸式视频游戏的最新水平。我们的目标是建立一个信息丰富、具有预测性的玩家状态，其中包括过去的游戏行为、品味、人口统计信息和当前的游戏轨迹。这种状态将用于个性化和以其他方式改进游戏方面，如动态难度缩放、非玩家角色人工智能(AI)和多人配对。我们特别关注收集和分析来自下一代控制器的数据，例如微软的Natal 3D相机系统。我们建议在培训所谓的深层架构方面以最近的发现为基础，这种架构可以利用大量数据(即使不需要手动标记)。这些体系结构能够学习数据中存在的主要变化解释因素的强大表示，一旦这些因素被拆分，就更容易学习如何使预测对游戏中的直接反馈有用。我们将与育碧员工一起，将我们的新算法整合到育碧自己的开发框架中，为游戏设计师提供构建下一代自适应游戏的强大新工具。

In collaboration with the video game manufacturer Ubisoft (Montreal) we will develop machine learning algorithms which improve the state-of-the-art in immersive video games. Our goal is to build an informative, predictive player state that incorporates past gaming behavior, tastes, demographic information, and current game play trajectory. This state will be used to personalize and otherwise improve game aspects such as dynamic difficulty scaling, non-player character artificial intelligence (AI) and multiplayer matchmaking. We place particular focus on the collection and analysis of data from next-generation controllers such the Natal 3D camera system from Microsoft. We propose to build upon recent discoveries in training so-called deep architectures, which can take advantage of large amounts of data (even without manual labeling). These architectures are able to learn powerful representations of the main explanatory factors of variation present in the data, and once these are pulled apart, it is easier to learn to make the predictions useful for direct in-game feedback. In concert with Ubisoft employees, we will integrate our new algorithms into Ubisoft's own development framework so as to provide game designers with powerful new tools for constructing the next generation of adaptive games.