Automated Sifting of Stories from Simulated Storyworlds

Wilkins Leong; Julie Porteous; John Thangarajah

IJCAI 2022poster4 citations

Automated Sifting of Stories from Simulated Storyworlds

Wilkins Leong, Julie Porteous, John Thangarajah

Abstract

Story sifting (or story recognition) allows for the exploration of events, stories, and patterns that emerge from simulated storyworlds. The goal of this work is to reduce the authoring burden for creating sifting queries. In this paper, we use the event traces of simulated storyworlds to create Dynamic Character Networks that track the changing relationship scores between characters in a simulation. These networks allow for the fortunes between any two characters to be plotted against time as a story arc. Similarity scores between story arcs from the simulation and a user’s query arc can be calculated using the Dynamic Time Warping algorithm. Events corresponding to the story arc that best matches the query arc can then be returned to the user, thus providing an intuitive means for users to sift a variety of stories without coding a search query. These components are implemented in our experimental prototype ARC SIFT. The results of a user study support our expectation that ARC SIFT is an intuitive and accurate tool that allows human users to sift stories out from a larger chronicle of events emerging from a simulated story world.

Application domains: Text, literature and creative languageApplication domains: User interfacesTheory and philosophy of arts and creativity in AI systems: Support of human creativityTheory and philosophy of arts and creativity in AI systems: Autonomous creative or artistic AIApplication domains: IdeationApplication domains: GamesTheory and philosophy of arts and creativity in AI systems: Evaluation of artistic or creative outputs, or of systemsMethods and resources: Other methods or resources

BibTeX

@inproceedings{ijcai2022p686,
  title     = {Automated Sifting of Stories from Simulated Storyworlds},
  author    = {Leong, Wilkins and Porteous, Julie and Thangarajah, John},
  booktitle = {Proceedings of the Thirty-First International Joint Conference on
               Artificial Intelligence, {IJCAI-22}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Lud De Raedt},
  pages     = {4950--4956},
  year      = {2022},
  month     = {7},
  note      = {AI and Arts},
  doi       = {10.24963/ijcai.2022/686},
  url       = {https://doi.org/10.24963/ijcai.2022/686},
}