Play-style Identification and Player Modelling for Generating Tailored Advice in Video Games
| dc.contributor.author | Ingram, Branden Corwin | |
| dc.contributor.supervisor | Rosman, Benjamin | |
| dc.contributor.supervisor | Van Alten, Clint | |
| dc.contributor.supervisor | Klein, Richard | |
| dc.date.accessioned | 2024-11-22T16:52:59Z | |
| dc.date.available | 2024-11-22T16:52:59Z | |
| dc.date.issued | 2023-09 | |
| dc.description | A thesis submitted in fulfilment of the requirements of the degree of Doctor of Philosophy, to the Faculty of Science, School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, 2023. | |
| dc.description.abstract | Recent advances in fields such as machine learning have enabled the development of systems that are able to achieve super-human performance on a number of domains, specifically in complex games such as Go and StarCraft. Based on these successes, it is reasonable to ask if these learned behaviours could be utilised to improve the performance of humans on the same tasks. However, the types of models used in these systems are typically not easily interpretable, and can not be directly used to improve the performance of a human. Additionally, humans tend to develop stylistic traits based on preference which aid in solving problems or competing at high levels. This thesis looks to address these difficulties by developing an end-to-end pipeline that can provide beneficial advice tailored to a player’s style in a video game setting. Towards this end, we demonstrate the ability to firstly cluster variable length multi-dimensional gameplay trajectories with respect to play-style in an unsupervised fashion. Secondly, we demonstrate the ability to learn to model an individual player’s actions during gameplay. Thirdly we demonstrate the ability to learn policies representative of all the play-styles identified with an environment. Finally, we demonstrate how the utilisation of these components can generate advice which is tailored to the individual’s style. This system would be particularly useful for improving tutorial systems that quickly become redundant lacking any personalisation. Additionally, this pipeline serves as a way for developers to garner insights on their player base which can be utilised for more informed decision-making on future feature releases and updates. For players, they gain a useful tool which can be utilised to learn how to play better as well identify as the characteristics of their gameplay as well as opponents. Furthermore, we contend that our approach has the potential to be employed in a broad range of learning domains. | |
| dc.description.submitter | MMM2024 | |
| dc.faculty | Faculty of Science | |
| dc.identifier | 0000-0001-7376-1327 | |
| dc.identifier.citation | Ingram, Branden Corwin. (2023). Play-style Identification and Player Modelling for Generating Tailored Advice in Video Games. [PhD thesis, University of the Witwatersrand, Johannesburg]. https://hdl.handle.net/10539/42853 | |
| dc.identifier.uri | https://hdl.handle.net/10539/42853 | |
| dc.language.iso | en | |
| dc.publisher | University of the Witwatersrand, Johannesburg | |
| dc.rights | ©2023 University of the Witwatersrand, Johannesburg. All rights reserved. The copyright in this work vests in the University of the Witwatersrand, Johannesburg. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of University of the Witwatersrand, Johannesburg. | |
| dc.rights.holder | University of the Witwatersrand, Johannesburg | |
| dc.school | School of Computer Science and Applied Mathematics | |
| dc.subject | Play-style Identification | |
| dc.subject | Player Modelling Advice Generation | |
| dc.subject | Unsupervised Clustering Machine Learning | |
| dc.subject | Video games | |
| dc.subject | Video games | |
| dc.subject | UCTD | |
| dc.subject.other | SDG-9: Industry, innovation and infrastructure | |
| dc.title | Play-style Identification and Player Modelling for Generating Tailored Advice in Video Games | |
| dc.type | Thesis |