Learning to influence emotional responses for interactive storytelling

We present an architecture for interactive storytelling. The system interleaves pre-authored text and videos to generate a story. Between iterations, the player is given an opportunity to answer questions that help to drive the narrative. Videos are used to have an effect on the emotional response of the players. The system is capable of performing modeling of both videos and players to better adapt the narrative progression in response to the player's answers to questions. The system is designed to serve two purposes: 1) to label natural language utterances and passages for use in a text classification system; and 2) to serve as a test environment for computational models of influence and persuasion. We motivate the approach we have taken by describing research efforts on classifying emotion in natural language and on the use of influence to affect decision making. The architecture and prototype system is described in detail. A summary of human-subject experiments planned are included as well.