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- W3094396102 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." @default.
- W3094396102 created "2020-10-29" @default.
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- W3094396102 date "2020-08-01" @default.
- W3094396102 modified "2023-09-24" @default.
- W3094396102 title "A Metric Learning Approach to Anomaly Detection in Video Games" @default.
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- W3094396102 doi "https://doi.org/10.1109/cog47356.2020.9231700" @default.
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