Details

Autor(en) / Beteiligte

• Hooshyar, Danial
• Yousefi, Moslem
• Lim, Heuiseok

Titel

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

Ist Teil von

• ACM computing surveys, 2018-01, Vol.50 (6), p.1-19

Ort / Verlag

Baltimore: Association for Computing Machinery

Erscheinungsjahr

2018

Quelle

Business Source Ultimate

Beschreibungen/Notizen

• 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.