Anomaly Detection in Video Games. / Wilkins, Benedict; Watkins, Chris; Stathis, Kostas.

In: ArXiv.org, 20.05.2020, p. 1-4.

Research output: Contribution to journalArticle

Published

Standard

Anomaly Detection in Video Games. / Wilkins, Benedict; Watkins, Chris; Stathis, Kostas.

In: ArXiv.org, 20.05.2020, p. 1-4.

Research output: Contribution to journalArticle

Harvard

APA

Vancouver

Wilkins B, Watkins C, Stathis K. Anomaly Detection in Video Games. ArXiv.org. 2020 May 20;1-4. 2005.10211.

Author

BibTeX

@article{7f2c6a7538304e97b96684fe85426c3c,
title = "Anomaly Detection in Video Games",
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. ",
keywords = "cs.LG, stat.ML",
author = "Benedict Wilkins and Chris Watkins and Kostas Stathis",
note = "4 pages, 3 figures, submitted to IEEE CONFERENCE ON GAMES (COG), Dataset https://www.kaggle.com/benedictwilkinsai/atari-anomaly-dataset-aad , Code and pre-trained models https://github.com/BenedictWilkinsAI/S3N",
year = "2020",
month = may
day = "20",
language = "English",
pages = "1--4",
journal = "ArXiv.org",

}

RIS

TY - JOUR

T1 - Anomaly Detection in Video Games

AU - Wilkins, Benedict

AU - Watkins, Chris

AU - Stathis, Kostas

N1 - 4 pages, 3 figures, submitted to IEEE CONFERENCE ON GAMES (COG), Dataset https://www.kaggle.com/benedictwilkinsai/atari-anomaly-dataset-aad , Code and pre-trained models https://github.com/BenedictWilkinsAI/S3N

PY - 2020/5/20

Y1 - 2020/5/20

N2 - 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.

AB - 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.

KW - cs.LG

KW - stat.ML

M3 - Article

SP - 1

EP - 4

JO - ArXiv.org

JF - ArXiv.org

M1 - 2005.10211

ER -