Affect detection from open-ended improvisational text

Abstract

We report progress on adding affect-detection to a program for virtual dramatic improvisation, monitored by a human director. We have developed an affect-detection module to control an automated virtual actor and to contribute to the automation of directorial functions. The work also involves basic research into how affect is conveyed through metaphor. The relevance of the project to the symposium is mainly in the application of AI to the creation of emotionally believable synthetic agents for interactive narrative environments, and in the study of the language used in improvisatory story-construction.