Affective modeling in behavioral simulations: Experience and implementations

Abstract

Recent studies have convincingly demonstrated the critical role of affect in human cognitive development and expression, supporting the case for incorporating affective representation into behavioral simulations for artificial intelligence. Music provides a powerful and concise mechanism for evoking and indeed representing emotions, and thus studying the ways in which music represents affect can provide insights into computer representations. That music can be understood as a multidimensional structure leads to the consideration of systemic grammars for this representation. A systemic grammar of emotions is presented which has proven effective as the basis for a concrete - and marketable implementation of behavioral simulations for virtual characters, by allowing the system to parse interactions between characters into representations of emotional states, and using the attributes of those determined states as determinants of subsequent behavior. © Springer-Verlag Berlin Heidelberg 2005.