视频游戏中的异常检测（CS ML）---用户7305506

为了设计有助于视频游戏质量保证过程的自动化工具，我们将识别视频游戏中的错误的问题归结为异常检测（AD）问题。 在这种情况下，我们将州际暹罗网络（S3N）开发为一种有效的AD深度度量学习方法，并探索如何将其用作自动化测试工具的一部分。 最后，通过对一系列Atari游戏的经验评估，我们发现S3N能够学习有意义的嵌入，因此能够识别各种常见类型的视频游戏错误。

原文标题：Anomaly Detection in Video Games

原文：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.

原文作者：Benedict Wilkins, Chris Watkins, Kostas Stathis

原文地址：https://arxiv.org/abs/2005.10211