{
  "id": "2005.10211",
  "version": "v1",
  "published": "2020-05-20T17:23:21.000Z",
  "updated": "2020-05-20T17:23:21.000Z",
  "title": "Anomaly Detection in Video Games",
  "authors": [
    "Benedict Wilkins",
    "Chris Watkins",
    "Kostas Stathis"
  ],
  "comment": "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",
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-05-20T17:23:21.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "anomaly detection",
      "video game quality assurance process",
      "efficient deep metric learning approach",
      "video game bugs",
      "state-state siamese networks"
    ],
    "tags": [
      "conference paper",
      "github project"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 4,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}