A Systematic Analysis of Unsupervised, Supervised, Few-Shot, and Neural Networks in Understanding Player Skill in Video Games

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dc.contributor.authorAdams, Rilan
dc.contributor.departmentDepartment of Applied Computer Science & Information Systemsen_US
dc.date.accessioned2023-05-15T15:11:57Z
dc.date.available2023-05-15T15:11:57Z
dc.date.issued2023-05-10
dc.description.abstractThe video game industry has grown massively. With leaderboard and player statistics becoming readily available, understanding player skill levels can aid in making market strategies, promotions, and advertisements decisions. This research aims to supply a systematic examination of how effective unsupervised, supervised, combined, few-shot, and neural networks (NN) are in automatically deciding player levels in video games. We focus on first-person shooter games such as Call of Duty (COD) and Tom Clancy’s Rainbow Six Siege (RSS). In addition to a systematic performance comparison, we aim to understand methods to reduce the human labeling effort in the work by using two sets of player data. Neural network frameworks such as TensorFlow are viewed, and feature selection of machine learning techniques is called for.en_US
dc.format.extent59 pagesen_US
dc.genrethesesen_US
dc.identifierdoi:10.13016/m2yvin-0f2v
dc.identifier.urihttp://hdl.handle.net/11603/27897
dc.language.isoen_USen_US
dc.relation.isAvailableAtFrostburg State Universityen_US
dc.rightsThe author owns the copyright to this work. This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by FSU for non-commercial research and education. For permission to publish or reproduce, please contact the author.en_US
dc.subjectvideo gamesen_US
dc.subjectcomputer scienceen_US
dc.subjectvideo games -- first person shootersen_US
dc.subjectneutral networksen_US
dc.subjectsupervised learningen_US
dc.subjectunsupervised learningen_US
dc.titleA Systematic Analysis of Unsupervised, Supervised, Few-Shot, and Neural Networks in Understanding Player Skill in Video Gamesen_US
dc.typeTexten_US

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