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 language | English |
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Article number | 90 |
Journal | ACM Computing Surveys |
Volume | 50 |
Issue number | 6 |
DOIs | |
Publication status | Published - 2018 Jan |
Bibliographical note
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