survey

Data-Driven Approaches to Game Player Modeling: A Systematic Literature Review

Authors:

Danial Hooshyar,

Moslem Yousefi,

Heuiseok LimAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 50, Issue 6

Article No.: 90, Pages 1 - 19

https://doi.org/10.1145/3145814

Published: 03 January 2018 Publication History

Abstract

Modeling and predicting player behavior is of the utmost importance in developing games. Experience has proven that, while theory-driven approaches are able to comprehend and justify a model's choices, such models frequently fail to encompass necessary features because of a lack of insight of the model builders. In contrast, data-driven approaches rely much less on expertise, and thus offer certain potential advantages. Hence, this study conducts a systematic review of the extant research on data-driven approaches to game player modeling. To this end, we have assessed experimental studies of such approaches over a nine-year period, from 2008 to 2016; this survey yielded 46 research studies of significance. We found that these studies pertained to three main areas of focus concerning the uses of data-driven approaches in game player modeling. One research area involved the objectives of data-driven approaches in game player modeling: behavior modeling and goal recognition. Another concerned methods: classification, clustering, regression, and evolutionary algorithm. The third was comprised of the current challenges and promising research directions for data-driven approaches in game player modeling.

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 50, Issue 6

November 2018

752 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3161158

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering / University of Florida / Gainesville, FL

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Copyright © 2018 ACM.

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Association for Computing Machinery

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Publication History

Published: 03 January 2018

Accepted: 01 September 2017

Revised: 01 May 2017

Received: 01 October 2016

Published in CSUR Volume 50, Issue 6

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Broadbent ACorradini A(2024)Using Genetic Algorithms for the Generation of Increasingly Challenging Terrain for Players to Navigate2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)10.1109/EAIS58494.2024.10570021(1-8)Online publication date: 23-May-2024
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