Year: 2024
Type: conference-paper
Abstract: Incremental story sifters analyze an in-progress simulation to extract interesting narrative content or find a set of events that have the potential to become more narratively interesting if followed up in a certain way. There has been some investigation on the potential for an incremental story sifter to suggest future narrative events to human authors, but the technique of guiding a simulation using story sifting, without any human interference, remains completely unexplored. Thus, we present Shepherd, a drama manager powered by incremental story sifting, which guides otherwise completely autonomous characters toward making narratively interesting choices.
Authors: Sage Deo, Jonathan Chung, Joshua McCoy
Institution: University of California, Davis
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4404445418