Diversifying the storytelling using Bayesian networks

Abstract

Storytelling is to generate a logical narrative that connects a sequence of events in the story. While the narrative coherence is the most important element in the storytelling, the diversity prolongs the story life and elicits interests on interpreting the story. In this paper, we investigate the narrative diversity given that the storytelling process is modelled by Bayesian networks. Bayesian networks structure causal relations between variables using a graphical representation, which facilitates communication between story generators and readers. The storytelling is equivalent to propagating the events in Bayesian networks, which preserves the narrative coherence. By adding the sampling process in the propagation, we can see the emergence of the narrative diversity in the storytelling. We study the entire process for a plot in one classical Chinese tale.