We present World of Bugs (WOB), an open platform that aims to support
Automated Bug Detection (ABD) research in video games. We discuss some open
problems in ABD and how they relate to the platform's design, arguing that
learning-based solutions are required if further progress is to be made. The
platform's key feature is a growing collection of common video game bugs that
may be used for training and evaluating ABD approaches.

我们提出了世界虫子（WOB），这是一个支持视频游戏自动化缺陷检测研究的开放平台。该平台的关键特色是一个共同的视频游戏缺陷收集，可用于训练和评估基于学习的解决方案。

World of Bugs: 3D 视频游戏自动漏洞检测平台

World of Bugs: A Platform for Automated Bug Detection in 3D Video Games

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。