Data-Driven Approaches to Game Player Modeling: A Systematic Literature Review
- Authors
- Hooshyar, Danial; Yousefi, Moslem; Lim, Heuiseok
- Issue Date
- 1월-2018
- Publisher
- ASSOC COMPUTING MACHINERY
- Keywords
- Game player modeling; data-driven approaches; computational models; systematic literature review (SLR)
- Citation
- ACM COMPUTING SURVEYS, v.50, no.6
- Indexed
- SCIE
SCOPUS
- Journal Title
- ACM COMPUTING SURVEYS
- Volume
- 50
- Number
- 6
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/78547
- DOI
- 10.1145/3145814
- ISSN
- 0360-0300
- 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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Collections - Graduate School > Department of Computer Science and Engineering > 1. Journal Articles
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