Title:
A computational model of suspense for the augmentation of intelligent story generation

Thumbnail Image
Author(s)
O'Neill, Brian
Authors
Advisor(s)
Riedl, Mark O.
Advisor(s)
Editor(s)
Associated Organization(s)
Organizational Unit
Organizational Unit
Series
Supplementary to
Abstract
In this dissertation, I present Dramatis, a computational human behavior model of suspense based on Gerrig and Bernardo's de nition of suspense. In this model, readers traverse a search space on behalf of the protagonist, searching for an escape from some oncoming negative outcome. As the quality or quantity of escapes available to the protagonist decreases, the level of suspense felt by the audience increases. The major components of Dramatis are a model of reader salience, used to determine what elements of the story are foregrounded in the reader's mind, and an algorithm for determining the escape plan that a reader would perceive to be the most likely to succeed for the protagonist. I evaluate my model by comparing its ratings of suspense to the self-reported suspense ratings of human readers. Additionally, I demonstrate that the components of the suspense model are sufficient to produce these human-comparable ratings.
Sponsor
Date Issued
2013-11-18
Extent
Resource Type
Text
Resource Subtype
Dissertation
Rights Statement
Rights URI