We introduced a new framework to detect perceptual bugs using a Long
Short-Term Memory (LSTM) network, which detects bugs in video games as
anomalies. The detected buggy frames are then clustered to determine the
category of the occurred bug. The framework was evaluated on two First Person
Shooter (FPS) games. Results show the effectiveness of the framework.

我们提出了一个新的框架，使用长短期记忆（LSTM）网络来检测感知缺陷，将视频游戏中的缺陷检测为异常，并通过聚类来确定发生的缺陷类别。该框架在两个第一人称射击游戏上进行了评估，结果显示了框架的有效性。

使用 LSTM 网络自动检测游戏中的错误

Automatic Bug Detection in Games using LSTM Networks

Automated Bug Detection (ABD) in video games is composed of two distinct but
complementary problems: automated game exploration and bug identification.
Automated game exploration has received much recent attention, spurred on by
developments in fields such as reinforcement learning. The complementary
problem of identifying the bugs present in a player's experience has for the
most part relied on the manual specification of rules. Although it is widely
recognised that many bugs of interest cannot be identified with such methods,
little progress has been made in this direction. In this work we show that it
is possible to identify a range of perceptual bugs using learning-based methods
by making use of only the rendered game screen as seen by the player. To
support our work, we have developed World of Bugs (WOB) an open platform for
testing ABD methods in 3D game environments.

本论文提出了利用学习方法，基于玩家所看到的渲染游戏屏幕，能够识别一系列感知错误的可能性，从而解决自动化游戏 bug 检测的问题，并且开发了一个名为 “World of Bugs” 的开放平台来测试该方法。