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Applying Narrative Theory to Aid Unexpectedness in a Self-Evaluative Story Generation System

Pickering, Todd and Jordanous, Anna (2017) Applying Narrative Theory to Aid Unexpectedness in a Self-Evaluative Story Generation System. In: ICCC'17 - Proceedings of the Eighth International Conference on Computational Creativity. Georgia Institute of Technology, Atlanta, Georgia, USA, pp. 213-220. ISBN 978-0-692-89564-1.

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Abstract

Predictability is the polar opposite of originality, and as such it is a notable obstacle that should be overcome in the pursuit of computational creativity. Accurately modelling a human’s understanding of predictability would be a monumental task, requiring a contextually rich network of social interaction, literature, news, and media. However, by artificially instilling a computer with some basic ideas about what is predictable in a given scenario, it can begin to gain an understanding of how to subvert expectation.

This project attempts to implement such a process into a specially designed story generation system known as Chronicle, inspired by Vladímir Propp’s Morphology of the Folk Tale. Chronicle aims to fine-tune narrative direction and progression in a system modelled on predictability.

Decisions made during the story generation process are based on probabilities defined by the expectations of the typical reader, and are amassed to formulate an overall predictability rating. The decision making process is manipulated by the system in order to pursue a customisable predictability target.

Chronicle was demonstrably accurate at evaluating its output in some cases, and less accurate in other cases. Further refinement is required to increase its efficacy, but it presents a promising step towards negotiating predictability in computational creativity.

Item Type: Book section
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76 Computer software > QA76.76.I59 Interactive media, hypermedia
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Anna Jordanous
Date Deposited: 09 May 2017 09:57 UTC
Last Modified: 17 Sep 2019 11:09 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/61661 (The current URI for this page, for reference purposes)
Jordanous, Anna: https://orcid.org/0000-0003-2076-8642
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