论文标题
视频游戏中检测异常检测的公制方法
A Metric Learning Approach to Anomaly Detection in Video Games
论文作者
论文摘要
为了设计有助于视频游戏质量保证过程的自动化工具，我们将视频游戏中的错误确定为异常检测（AD）问题的问题。在这种情况下，我们开发了一种有效的深度度量学习方法，并探讨如何将其用作自动测试工具的一部分。最后，我们通过经验评估在一系列Atari游戏中展示，S3N能够学习有意义的嵌入，因此能够识别各种常见的视频游戏错误。
With the aim of designing automated tools that assist in the video game quality assurance process, we frame the problem of identifying bugs in video games as an anomaly detection (AD) problem. We develop State-State Siamese Networks (S3N) as an efficient deep metric learning approach to AD in this context and explore how it may be used as part of an automated testing tool. Finally, we show by empirical evaluation on a series of Atari games, that S3N is able to learn a meaningful embedding, and consequently is able to identify various common types of video game bugs.
