Toward a Computational Model of Affective Responses to Stories for Augmenting Narrative Generation

Current approaches to story generation do not utilize models of human affect to create stories with dramatic arc, suspense, and surprise. This paper describes current and future work towards computational models of affective responses to stories for the purpose of augmenting computational story generators. I propose two cognitively plausible models of suspense and surprise responses to stories. I also propose methods for evaluating these models by comparing them to actual human responses to stories. Finally, I propose the implementation of these models as a heuristic in a search-based story generation system. By using these models as a heuristic, the story generation system will favor stories that are more likely to produce affective responses from human readers.