Title
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.
Year
DOI
Venue
2014
10.1007/978-3-319-20230-3_9
AGENTS AND DATA MINING INTERACTION (ADMI 2014)
Keywords
Field
DocType
Interactive storytelling,Bayesian networks,Diversity
Sampling process,Storytelling,Computer science,Cognitive science,Causal relations,Coherence (physics),Narrative,Bayesian network,Artificial intelligence,Classical Chinese,Interactive storytelling,Machine learning
Conference
Volume
ISSN
Citations 
9145
0302-9743
0
PageRank 
References 
Authors
0.34
3
5
Name
Order
Citations
PageRank
Wenyun Wu100.34
Biyang Ma200.68
Shaoxin Zhang300.34
Yifeng Zeng441543.27
Hua Mao511111.53