Modeling and Evaluating a Bayesian Network of Culture-Dependent Behaviors

Anthropomorphic user interfaces such as virtual agents or humanoid robots aim on simulating believable human behavior. As human behavior is influenced by diversifying factors such as cultural background, research in anthropomorphic user interfaces considers culture background for their behavioral models as well. This paper presents a hybrid approach of creating a culture-specific model of non-verbal behaviors for simulated dialogs based on both: theoretical knowledge and empirical data. Therefore, the structure and variables of a Bayesian network are designed based on models and theories from the social sciences, while its parameters are learned from a video corpus of German and Japanese conversations in first time meeting scenarios. To validate the model a 10-fold-cross-validation has been conducted, suggesting that with the model culture-specific behavior can automatically be generated for some of the investigated behavioral aspects.