Player Style Clustering without Game Variables

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Player clustering when applied to the field of video games has several potential applications. For example, the evaluation of the composition of a player base or the generation of AI agents with identified playing styles. These agents can then be used for either the testing of new game content or used directly to enhance a player’s gaming experience. Most current player clustering techniques focus on the use of internal game variables. This raises two main issues: (1) the availability of game variables, as source code access is required to log them and hence limits the data sources that can be used, and (2) the choice of game variables can introduce unintended bias in the types of play style extracted. In this work, a hybrid unsupervised frame encoder and a ‘reference-based’ clustering algorithm are both proposed and combined to allow clustering from raw game play videos. It is shown that the proposed methods are most beneficial when the types of play styles are unknown.
Original languageEnglish
Title of host publicationProceedings of the International Conference on the Foundations of Digital Games (FDG) 2020
Publication statusAccepted/In press - 28 May 2020

Bibliographical note

©2020 Copyright held by the owner/author(s). This is an author-produced version of the published paper. Uploaded with permission of the publisher/copyright holder. Further copying may not be permitted; contact the publisher for details

Cite this