A Metric Learning Approach to Anomaly Detection in Video Games

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publication2020 IEEE Conference on Games (CoG)
Number of pages4
ISBN (Electronic)978-1-7281-4533-4
ISBN (Print)978-1-7281-4534-1
Publication statusPublished - 20 Oct 2020

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