  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.
