Player Style Clustering without Game Variables

Mark Ferguson; Sam Devlin; Daniel Kudenko; James Alfred Walker

Player Style Clustering without Game Variables

Mark Ferguson, Sam Devlin, Daniel Kudenko, James Alfred Walker

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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 language	English
Title of host publication	Proceedings of the International Conference on the Foundations of Digital Games (FDG) 2020
Publisher	ACM
Publication status	Accepted/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

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FDG__Short____Player_Style_Clustering_without_Game_Variables (1)
©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
Accepted author manuscript, 675 KBLicence: CC BY

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Cowling, P. I. (Principal investigator), Austin, J. (Co-investigator), Cairns, P. A. (Co-investigator), Holliman, N. S. (Co-investigator), Hook, J. D. (Co-investigator), Marsden, E. (Co-investigator), Murphy, D. T. (Co-investigator), Petrie, H. (Co-investigator), Reed, D. J. (Co-investigator), Richards, J. D. (Co-investigator), Ursu, M. (Co-investigator), Wade, A. (Co-investigator), Baier, H. (Researcher), Beale, G. (Researcher), Block, F. O. (Researcher), Deterding, C. S. (Researcher), Devlin, S. (Researcher), Drachen, A. (Researcher), Kasprowicz, R. E. (Researcher), Smith, D. P. (Researcher) & Williams, D. A. H. (Researcher)
EPSRC
1/10/15 → 30/09/22
Project: Research project (funded) › Research

Cite this

@inproceedings{fad56c816a22499fb1a3d8511219b35d,

title = "Player Style Clustering without Game Variables",

abstract = "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{\textquoteright}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 {\textquoteleft}reference-based{\textquoteright} 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.",

author = "Mark Ferguson and Sam Devlin and Daniel Kudenko and Walker, {James Alfred}",

note = "{\textcopyright}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 ",

year = "2020",

month = may,

day = "28",

language = "English",

booktitle = "Proceedings of the International Conference on the Foundations of Digital Games (FDG) 2020",

publisher = "ACM",

address = "United States",

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TY - GEN

T1 - Player Style Clustering without Game Variables

AU - Ferguson, Mark

AU - Devlin, Sam

AU - Kudenko, Daniel

AU - Walker, James Alfred

N1 - ©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

PY - 2020/5/28

Y1 - 2020/5/28

N2 - 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.

AB - 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.

M3 - Conference contribution

BT - Proceedings of the International Conference on the Foundations of Digital Games (FDG) 2020

PB - ACM

ER -

Player Style Clustering without Game Variables

Abstract

Bibliographical note

Access to Document

Projects

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Cite this