(a breakdown of useful info from research articles)

Gervás, P., Lönneker-Rodman, B., Meister, J. C., & Peinado, F. (2006, May). Narrative models: Narratology meets artificial intelligence. In International Conference on Language Resources and Evaluation. Satellite Workshop: Toward Computational Models of Literary Analysis (pp. 44-51).

Gervás et al discuss the various narrative models from the perspectives of the humanities and computer science. It focuses on the way in which these models address such a complex problem, and how various algorithms based upon these models have functioned. This article is significant because it provides a broad framework of information. I will be using it in my literature review to generate ideas for discussion and to find other articles.

Gervás, P. (2009). Computational approaches to storytelling and creativity. AI Magazine, 30(3), 49-62.

Presented in AI Magazine, this article is addressed to those of an intellectual background, who have no prior exposure to this specific field. Gervás does an excellent job of defining important, yet common terms to better restrict the field of study. He also provides specific examples for different methodologies, and describes the Fabulist model well.

Gervás, P. (2016). Computational drafting of plot structures for Russian folk tales. Cognitive Computation, 8(2), 187-203.

Propp was one of the earliest creators of Narratology, or the study of narratives. This article breaks down and explains the applications of one of Propp’s largest undertakings: an extensive formulaic analysis of folk tales from his native Russia. This article gives great history and extensive detail about the models that stem from Propp and the implications of his work.

Lönneker, B., Meister, J. C., Gervás, P., Peinado, F., & Mateas, M. (2005). Story generators: Models and approaches for the generation of literary artefacts. In the 17th Joint International Conference of the Association for Computers and the Humanities and the Association for Literary and Linguistic Computing (pp. 126-133).

These authors provide details about the ideal story generator algorithms, with detailed descriptions of data representation and general flow for generative systems. This article also contributes details about a system using Proppian morphology and case-based artificial intelligence to generate narratives. I will use this article to provide a discussion on data representation and will also use the ProtoPropp system as a specific example.

Kybartas, B., & Bidarra, R. (2017). A survey on story generation techniques for authoring computational narratives. IEEE Transactions on Computational Intelligence and AI in Games, 9(3), 239-253.

Kybartas and Bidarra investigate the potential that mixed-initiative work has in the field of computational creativity. Mixed-initiative refers to the combined efforts of the author and the SGA in the creation of narrative artefacts. They also emphasize the elements of plot and space, and the balance of human vs. computer influence on these two areas.

Peinado, F. (2006). Evaluation of automatic generation of basic stories. New Generation Computing, 24(3), 289-302.

In this article, Peinado uses a particular system that had been developed to generate narratives. These narratives are then compared with original fables that exist in the knowledge base, and with randomly assembled parts of these fable representations. The article gives details about specific design choices and implementations of the system, as well as specific output examples.

Pérez Y Pérez, R. P. Ã, & Sharples, M. (2001). MEXICA: A computer model of a cognitive account of creative writing. Journal of Experimental and Theoretical Artificial Intelligence, 13(2), 119-139.

Pérez Y Pérez gives a specific analysis of the model MEXICA, which emphasizes the development of story through reactions to the current state of the world. MEXICA also accounts for more complex characters by representing a character’s emotional conflicts as well as their motives.

Pérez y Pérez, R., & Sharples, M. (2004). Three computer-based models of storytelling: BRUTUS, MINSTREL and MEXICA. Knowledge-Based Systems, 17(1), 15-29.

This article compares three of the more central models of SGAs, providing examples of their output and breaking down their strengths and weaknesses. The article provides a significant snapshot of how this field has changed over time. I will be using the models discussed in this paper as material for discussion in my literature review.