A guided journey through non-interactive automatic story generation

• URL: http://arxiv.org/abs/2110.11167v1
• Date: Fri, 8 Oct 2021 10:01:36 GMT
• Title: A guided journey through non-interactive automatic story generation
• Authors: Luis Miguel Botelho
• Abstract summary: The article presents requirements for creative systems, three types of models of creativity (computational, socio-cultural, and individual), and models of human creative writing. The article concludes that the autonomous generation and adoption of the main idea to be conveyed and the autonomous design of the creativity ensuring criteria are possibly two of most important topics for future research.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a literature survey on non-interactive computational story generation. The article starts with the presentation of requirements for creative systems, three types of models of creativity (computational, socio-cultural, and individual), and models of human creative writing. Then it reviews each class of story generation approach depending on the used technology: story-schemas, analogy, rules, planning, evolutionary algorithms, implicit knowledge learning, and explicit knowledge learning. Before the concluding section, the article analyses the contributions of the reviewed work to improve the quality of the generated stories. This analysis addresses the description of the story characters, the use of narrative knowledge including about character believability, and the possible lack of more comprehensive or more detailed knowledge or creativity models. Finally, the article presents concluding remarks in the form of suggestions of research topics that might have a significant impact on the advancement of the state of the art on autonomous non-interactive story generation systems. The article concludes that the autonomous generation and adoption of the main idea to be conveyed and the autonomous design of the creativity ensuring criteria are possibly two of most important topics for future research.

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