In their survey of authoring tools for computational narrative, Kybartas and Bidarra note that "we believe that creating a standard model of computational narrative could allow different systems to interact with the same narrative, without being restricted by incompatible models and definitions. Furthermore, such a model would also facilitate research into the generation of specific story components, e.g., allowing for multiple generators and even authors to collaborate on a given narrative" [Kybartas and Bidarra [2017]]. This paper proposes such a standard: the Universal Narrative Model (UNM). We foresee that generative AI will enable a new paradigm of storytelling technologies and processes: from assisting a writer of linear media (novels, film, television, etc.) by allowing them to test out scenes and characters before committing them to a script, all the way through to real-time storytelling systems in videogames which respond to a player's agency, and countless use cases in between [Peng et al. [2024]]. The UNM is designed to service any use case in which coherent narrative structure is a consideration, and in which authorial intent and direction is privileged. In the last five years, a robust body of research has demonstrated a wide variety of potential uses for computational narrative systems powered by generative AI, and some limited commercial deployments already exist [Yang et al. [2024], Hu et al. [2024]]. With such promise, however, comes a series of challenges: technical, narrative, and ethical. The goal of the Entertainment Technology Center's "Universal Narrative Model" project was to produce the UNM as an open standard. The ultimate directive of the project was to privilege, above all else, author-centric design and functionality, setting the stage for generative workflows which extend an author's narrative intent and creativity, rather than eclipse or replace it.

Academic trade requires juggling multiple variants of the same content published in different formats: manuscripts, presentations, posters and computational notebooks. The need to track versions to accommodate for the write--review--rebut--revise life-cycle adds another layer of complexity. We propose to significantly reduce this burden by maintaining a single source document in a version-controlled environment (such as git), adding functionality to generate a collection of output formats popular in academia. To this end, we utilise various open-source tools from the Jupyter scientific computing ecosystem and operationalise selected software engineering concepts. We offer a proof-of-concept workflow that composes Jupyter Book (an online document), Jupyter Notebook (a computational narrative) and reveal.js slides from a single markdown source file. Hosted on GitHub, our approach supports change tracking and versioning, as well as a transparent review process based on the underlying code issue management infrastructure. An exhibit of our workflow can be previewed at https://so-cool.github.io/you-only-write-thrice/.

From Young 2010), frames (Zhu and Ontañón 2014), plotpoints early games like Zork, to the text-based interactive Victorian (Weyhrauch and Bates 1997; Nelson and Mateas 2005; dramas generated by Versu (Evans and Short 2014) Sharma et al. 2010) or social models (McCoy et al. 2011), to 3D RPG games like Skyrim (Ruch 2011), the quality of the problem of how to computationally model narratives the stories play a crucial role in engaging the player and and story spaces remains open.

Computational Narrative has provided several examples of how to process narrations using semantical approaches. While many useful concepts for computational management of stories have been unveiled, a common barrier has hindered their development: semantic knowledge is still too complex to handle. In this paper, a focus shift based on narrative structure is proposed. Instead of digging deeper into the possibilities of semantic processing, analysing structural properties of stories and keeping the semantic load to a minimum can allow for a more efficient use of available narrative corpora, even without mimicking human behaviour.