Playing style recognition through an adaptive video game

Abstract

Playing style recognition is crucially important for style-based adaptation of digital games. Unlike traditional ways for measuring of styles by means of self-reports, automatic style estimation incorporated into a video game appears to be a more efficient and ecologically valid method. The article presents a model for in-game recognition of four playing styles (Competitor, Dreamer, Logician, and Strategist) based on the Kolb's experiential learning theory. The model applies multiple linear regression over task performance metrics as explanatory variables and coefficients found first by a heuristic approach relaying on experience and observation knowledge of domain experts and, next, estimated by the least squares method. Experiments with the model implemented within an affectively adaptive video game demonstrated the benefits of emotion-based dynamic difficulty adjustment over playing outcomes and proved its validity as an accurate instrument for automatic estimation of both the four playing styles and the learning styles of Honey and Mumford.