A Stochastic Approach To Character Growth In Automated Narrative Generation
Abstract
This paper describes the AESOP program, a modular storytelling framework that composes storyboards for stories that emphasize character development. Through a combination of modules designed to represent story elements, such as Characters, Actions, and Conditions. AESOP combines the novelty of stochastic events with the grounding of logic progression. These modules form a unique approach that synthesizes previous storytelling systems. A prototype system was built and tested in cooperation with the Fraunhofer Inst, for Digital Media Technology. Our tests experimented with the inclusion and removal of certain modules from the story origination process, and our results showed that the introduction of our modules improved the perceived quality of the generated stories over random story origination. Though the system has a number of shortcomings in terms of its details, readability, and flexibility, it makes great strides in automated story origination.
Publication Date
1-1-2017
Publication Title
FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference
Number of Pages
152-157
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
Copyright Status
Unknown
Socpus ID
85029538465 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85029538465
STARS Citation
Wade, Josh; Wong, Josiah; Waldor, Max; Pasqualin, Lucas; and Jantke, Klaus, "A Stochastic Approach To Character Growth In Automated Narrative Generation" (2017). Scopus Export 2015-2019. 6998.
https://stars.library.ucf.edu/scopus2015/6998