Characterizing Player's Experience From Physiological Signals Using Fuzzy Decision Trees

Florent Levillain (1) , Joseph Onderi Orero (2) , Maria Rifqi (2) , Bernadette Bouchon-Meunier (2)

1 LUTIN - Laboratoire des Usages en Technologies d'Information Numériques
2 MALIRE - Machine Learning and Information Retrieval

In the recent years video games have enjoyed a dramatic increase in popularity, the growing market being echoed by a genuine interest in the academic field. With this flourishing technological and theoretical efforts, there is need to develop new evaluative methodologies for acknowledging the various aspects of the player's subjective experience, and especially the emotional aspect. In this study, we addressed the possibility of developing a model for assessing the player's enjoyment (amusement) with respect to challenge in an action game. Our aim was to explore the viability of a generic model for assessing emotional experience during gameplay from physiological signals. In particular, we propose an approach to characterize the player's subjective experience in different psychological levels of enjoyment from physiological signals using fuzzy decision trees.

Domaines: Intelligence artificielle [cs.AI]

Florent Levillain, Joseph Onderi Orero, Maria Rifqi, Bernadette Bouchon-Meunier. Characterizing Player's Experience From Physiological Signals Using Fuzzy Decision Trees. CIG 2010 - IEEE Symposium on Computational Intelligence and Games, Aug 2010, Copenhagen, Denmark. pp.75 - 82, ⟨10.1109/ITW.2010.5593370⟩. ⟨hal-00589944⟩