Data-driven approaches to game player modeling: A systematic literature review

Danial Hooshyar, Moslem Yousefi, Heuiseok Lim

Research output: Contribution to journalReview articlepeer-review

88 Citations (Scopus)

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.

Original languageEnglish
Article number90
JournalACM Computing Surveys
Volume50
Issue number6
DOIs
Publication statusPublished - 2018 Jan

Bibliographical note

Funding Information:
This work is supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2016R1A2B2015912). Author’s addresses: D. Hooshyar and H. Lim, Lyceum, Department of Computer Science and Engineering, Korea University, Anam-ro, Seongbuk-gu, Seoul, the Republic of Korea; emails: [email protected], [email protected]; M. Yousefi, School of Civil, Environmental and Architectural Engineering, Korea University, Anam-ro, Seongbuk-gu, Seoul, the Republic of Korea; email: [email protected]. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. © 2018 ACM 0360-0300/2018/01-ART90 $15.00 https://doi.org/10.1145/3145814

Publisher Copyright:
© 2018 ACM.

Keywords

  • Computational models
  • Data-driven approaches
  • Game player modeling
  • Systematic literature review (SLR)

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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