A Metric Learning Approach to Anomaly Detection in Video Games
This work addresses the problem of automating quality assurance for video game developers, but it is incremental as it applies existing anomaly detection methods to a new domain.
The paper tackled the problem of identifying bugs in video games by framing it as an anomaly detection task, and they developed State-State Siamese Networks (S3N) as a deep metric learning approach, showing through empirical evaluation on Atari games that it can learn meaningful embeddings and identify common bug types.
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.