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

Danial Hooshyar, Moslem Yousefi, Heui Seok Lim

Research output: Contribution to journalReview article

35 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 1

Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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