A Synset-Based Recommender Method for Mixed-Initiative Narrative World Creation

Abstract

A narrative world (NW) is an environment which supports enacting a given story. Manually creating virtual NWs (e.g. for games and films) requires considerable creative and technical skills, in addition to a deep understanding of the story in question. Procedural generation methods, in turn, generally lack in creativity and have a hard time coping with the numerous degrees of freedom left open by a story. In contrast, mixed-initiative approaches offer a promising path to solve this tension. We propose a mixed-initiative approach assisting an NW designer in choosing plausible entities for the locations, where the story takes place. Our approach is based on a recommender method that uses common and novel associations to narrative locations, actions and entities. Our method builds upon a large dataset of co-occurrences of disambiguated terms that we retrieved from photo captions. Building on this knowledge, our solution deploys entity (un)relatedness, offers clusters of semantically and contextually related entities, and highlights novelty of recommended content, thus effectively supporting the designer's creative task, while helping to stay consistent with the story. We demonstrate our method via an interactive prototype called TaleForge. Designers can obtain meaningful entity suggestions for their NWs, which enables guided exploration, while preserving creative freedom. We present an example of the interactive workflow of our method, and illustrate its usefulness.