Shepherd: An Incremental Story Sifting-Based Drama Manager

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.