Narrative Plan Generation with Self-Supervised Learning

Authors

  • Mihai Polceanu University of Greenwich
  • Julie Porteous RMIT University
  • Alan Lindsay Heriot-Watt University
  • Marc Cavazza University of Greenwich

Keywords:

Game Design -- Procedural Content Generation & Storytelling

Abstract

Narrative Generation has attracted significant interest as a novel application of Automated Planning techniques. However, the vast amount of narrative material available opens the way to the use of Deep Learning techniques. In this paper, we explore the feasibility of narrative generation through self-supervised learning, using sequence embedding techniques or auto-encoders to produce narrative sequences. We use datasets of well-formed plots generated by a narrative planning approach, using pre-existing, published, narrative planning domains, to train generative models. Our experiments demonstrate the ability of generative sequence models to produce narrative plots with similar structure to those obtained with planning techniques, but with significant plot novelty in comparison with the training set. Most importantly, generated plots share structural properties associated with narrative quality measures used in Planning-based methods. As plan-based structures account for a higher level of causality and narrative consistency, this suggests that our approach is able to extend a set of narratives with novel sequences that display the same high-level narrative properties. Unlike methods developed to extend sets of textual narratives, ours operates at the level of plot structure. Thus, it has the potential to be used across various media for plots of significant complexity, being initially limited to training and generation operating in the same narrative genre.

Downloads

Published

2021-05-18

How to Cite

Polceanu, M., Porteous, J., Lindsay, A., & Cavazza, M. (2021). Narrative Plan Generation with Self-Supervised Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 35(7), 5984-5992. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16747

Issue

Section

AAAI Technical Track on Humans and AI