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

用于自动分析下一代视频游戏中玩家行为的机器学习算法

• 批准号: 396001-2009
• 负责人: Bengio, Yoshua
• 金额: $ 3.93万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=487338
• 关键词: Machine; learning; algorithms; automated; analysis

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.

通过与视频游戏制造商Ubisoft（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、预测性强的玩家状态，其中包含过去的游戏行为、品味、人口统计信息和当前的游戏轨迹。此状态将用于个性化和改进游戏方面，例如动态难度缩放，非玩家角色人工智能（AI）和多人匹配。我们特别关注下一代控制器的数据收集和分析，例如来自微软的纳塔尔3D相机系统。我们建议建立在训练所谓的深度架构的最新发现的基础上，这种架构可以利用大量数据（即使没有手动标记）。这些架构能够学习数据中存在的变化的主要解释因素的强大表示，一旦这些被分开，就更容易学习使预测对直接的游戏内反馈有用。我们将与育碧员工合作，将我们的新算法整合到育碧自己的开发框架中，为游戏设计师提供强大的新工具，以构建下一代自适应游戏。