A Metric Learning Approach to Anomaly Detection in Video Games
- URL: http://arxiv.org/abs/2005.10211v2
- Date: Wed, 1 Jul 2020 13:27:00 GMT
- Title: A Metric Learning Approach to Anomaly Detection in Video Games
- Authors: Benedict Wilkins, Chris Watkins, Kostas Stathis
- Abstract summary: We develop State-State Siamese Networks (S3N) as an efficient deep metric learning approach to anomaly detection.
We show by empirical evaluation on a series of Atari games, that S3N is able to learn a meaningful embedding.
- Score: 1.1602089225841632
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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