Affect Detection and an Automated Improvisational AI Actor in E-Drama

Enabling machines to understand emotions and feelings of the human users in their natural language textual input during interaction is a challenging issue in Human Computing. Our work presented here has tried to make our contribution toward such machine automation. We report work on adding affect-detection to an existing e-drama program, a text-based software system for dramatic improvisation in simple virtual scenarios, for use primarily in learning contexts. The system allows a human director to monitor improvisations and make interventions, for instance in reaction to excessive, insufficient or inappropriate emotions in the characters’ speeches. Within an endeavour to partially automate directors’ functions, and to allow for automated affective bit-part characters, we have developed an affect-detection module. It is aimed at detecting affective aspects (concerning emotions, moods, value judgments, etc.) of human-controlled characters’ textual “speeches”. The work also acco...

• Type: Conference
• Year: 2007
• Where: AIHC
• Authors: Li Zhang, Marco Gillies, John A. Barnden, Robert J. Hendley, Mark G. Lee, Alan M. Wallington
• Keywords: Affective Bit-part Character, Affective States, AIHC 2007, Applied Computing, Language Textual Input