research-article

An inclusive view of player modeling

Authors:

Kenneth Hullet,

Anne SullivanAuthors Info & Claims

FDG '11: Proceedings of the 6th International Conference on Foundations of Digital Games

Pages 301 - 303

https://doi.org/10.1145/2159365.2159419

Published: 29 June 2011 Publication History

Abstract

"Player modeling" is a loose concept. It can equally apply to everything from a predictive model of player actions resulting from machine learning to a designer's description of a player's expected reactions in response to some piece of game content. This lack of a precise terminology prevents practitioners from quickly finding introductions to applicable modeling methods or determining viable alternatives to their own techniques. We introduce a vocabulary that distinguishes between the major existing player modeling applications and techniques. Four facets together define the kind for a model: the scope of application, the purpose of use, the domain of modeled details, and the source of a model's derivation or motivation. This vocabulary allows the identification of relevant player modeling methods for particular problems and clarifies the roles that a player model can take.

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An inclusive view of player modeling

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cover image ACM Other conferences

FDG '11: Proceedings of the 6th International Conference on Foundations of Digital Games

June 2011

356 pages

ISBN:9781450308045

DOI:10.1145/2159365

Conference Chair:
Marc Cavazza
Teesside University, UK
,
Program Chairs:
Katherine Isbister
Polytechnic Institute of New York University
,
Charles Rich
Worcester Polytechnic Institute

Copyright © 2011 Authors.

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SASDG: Society for the Advancement of the Science of Digital Games

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 June 2011

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FDG'11

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FDG'11: Foundations of Digital Games

June 29 - July 1, 2011

Bordeaux, France

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FDG '11 Paper Acceptance Rate 31 of 107 submissions, 29%;

Overall Acceptance Rate 152 of 415 submissions, 37%

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https://dl.acm.org/doi/10.1145/3582437.3582448
Pfau JLiapis AYannakakis GMalaka R(2023)Dungeons & Replicants II: Automated Game Balancing Across Multiple Difficulty Dimensions via Deep Player Behavior ModelingIEEE Transactions on Games10.1109/TG.2022.316772815:2(217-227)Online publication date: Jun-2023
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https://doi.org/10.1109/TCSS.2021.3132355
Togelius JKhalifa AEarle SGreen MSoros L(2023)Evolutionary Machine Learning and GamesHandbook of Evolutionary Machine Learning10.1007/978-981-99-3814-8_25(715-737)Online publication date: 2-Nov-2023
https://doi.org/10.1007/978-981-99-3814-8_25
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Seif El-Nasr MKleinman E(2020)Data-Driven Game Development: Ethical ConsiderationsProceedings of the 15th International Conference on the Foundations of Digital Games10.1145/3402942.3402964(1-10)Online publication date: 15-Sep-2020
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