Modeling Individual Differences through Frequent Pattern Mining on Role-Playing Game Actions

Description:

Using an Role-Playing Game (RPG) with multiple affordances, we designed an experiment collecting granular in-game behaviors of players. Using sequential pattern mining and supervised learning, we developed a model that uses gameplay action sequences to predict the real world characteristics, including gender, game play expertise and five personality traits (as defined by psychology). The results show that game expertise is a dominant factor that impacts in-game behaviors.

 

Members of the Project:

Zhengxing Chen, Magy Seif El Nasr, Alessandro Canossa, Jeremy Badler, Stefanie Tignor, Randy Colvin

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