Generating narrative action schemas for suspense

A bottleneck in interactive storytelling is the authorial burden of writing narrative units, and connecting them to the interactive narrative structure. To address this problem, we present a hybrid approach that combines AI planning and evolutionary optimization in order to generated new plan operators representing possible story actions, within the framework of a planningbased interactive narrative system. We focus our work on inventing plan operators that are useful for contributing to suspenseful interactive stories, using suspense metrics that have been proposed in the literature.We devise an encoding scheme for converting a plan operator into a genetic-algorithm chromosome and vice versa, respecting constraints that are needed for an operator to be well-formed. We discuss the performance of the system, and several examples from preliminary experiments carried out to evaluate the evolved operators.This work has been supported in part by the EU FP7 ICT project SIREN (project no: 258453). We thank Arnav Jhala at UC Santa Cruz, and Antonios Liapis and Julian Togelius at IT University of Copenhagen for the discussion.peer-reviewe

Modifying Entity Relationship Models for Collaborative Fiction Planning and its Impact on Potential Authors

We propose a modified Entity Relationship (E-R) model, traditionally used for software engineering, to structure, store and share plot data. The flexibility of E-R modelling has been demonstrated by its decades of usage in a wide variety of situations. The success of the E-R model suggests that it could be useful for collaborating fiction authors, adding a certain degree of computational power to their process. We changed the E-R model syntax to better suit the story plans, switching the emphasis from generic types to instanced story entities, but preserving relationships and attributes. We conducted a small-scale basic experiment to study the impact of using our modified E-R model on authors when understanding and contributing into a pre-existing fiction story plan. The results analysis revealed that the E-R model supports authors as effectively as written text in reading comprehension, memory, and contributing. In addition, the results show that, when combined together, the written text and the E-R model help participants achieve better comprehension--always within the frame of our experiment. We discuss potential applications of these findings

On the Creativity of Large Language Models

Large Language Models (LLMs) are revolutionizing several areas of Artificial Intelligence. One of the most remarkable applications is creative writing, e.g., poetry or storytelling: the generated outputs are often of astonishing quality. However, a natural question arises: can LLMs be really considered creative? In this article we firstly analyze the development of LLMs under the lens of creativity theories, investigating the key open questions and challenges. Then, we discuss a set of "easy" and "hard" problems in machine creativity, presenting them in relation to LLMs. Finally, we examine the societal impact of these technologies with a particular focus on the creative industries

Authors' Values and Attitudes Towards AI-bridged Scalable Personalization of Creative Language Arts

Generative AI has the potential to create a new form of interactive media: AI-bridged creative language arts (CLA), which bridge the author and audience by personalizing the author's vision to the audience's context and taste at scale. However, it is unclear what the authors' values and attitudes would be regarding AI-bridged CLA. To identify these values and attitudes, we conducted an interview study with 18 authors across eight genres (e.g., poetry, comics) by presenting speculative but realistic AI-bridged CLA scenarios. We identified three benefits derived from the dynamics between author, artifact, and audience: those that 1) authors get from the process, 2) audiences get from the artifact, and 3) authors get from the audience. We found how AI-bridged CLA would either promote or reduce these benefits, along with authors' concerns. We hope our investigation hints at how AI can provide intriguing experiences to CLA audiences while promoting authors' values.Comment: 16 pages, 6 figures, 2 tables. Accepted to ACM CHI 202

A variant of tale-spin with independent data and rule bases

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A Narrative Sentence Planner and Structurer for Domain Independent, Parameterizable Storytelling

Storytelling is an integral part of daily life and a key part of how we share information and connect with others. The ability to use Natural Language Generation (NLG) to produce stories that are tailored and adapted to the individual reader could have large impact in many different applications. However, one reason that this has not become a reality to date is the NLG story gap, a disconnect between the plan-type representations that story generation engines produce, and the linguistic representations needed by NLG engines. Here we describe Fabula Tales, a storytelling system supporting both story generation and NLG. With manual annotation of texts from existing stories using an intuitive user interface, Fabula Tales automatically extracts the underlying story representation and its accompanying syntactically grounded representation. Narratological and sentence planning parameters are applied to these structures to generate different versions of the story. We show how our storytelling system can alter the story at the sentence level, as well as the discourse level. We also show that our approach can be applied to different kinds of stories by testing our approach on both Aesop’s Fables and first-person blogs posted on social media. The content and genre of such stories varies widely, supporting our claim that our approach is general and domain independent. We then conduct several user studies to evaluate the generated story variations and show that Fabula Tales’ automatically produced variations are perceived as more immediate, interesting, and correct, and are preferred to a baseline generation system that does not use narrative parameters

Generador de historias basado en agentes

En este proyecto discutiremos cómo podemos generar creatividad computacional. Esto es una tarea compleja, ya que el propio concepto de lo que es la creatividad es ambiguo, puesto que en él intervienen cosas como el talento, que son de por sí difíciles de explicar. Siguiendo el dominio de nuestra aplicación, nos concentraremos en el ámbito de la narrativa y la creación de historias. Veremos cómo podemos generar historias automáticamente, además de los diferentes generadores con los que nos hemos encontrado, y que nos han servido como fuente de inspiración en mayor o menor medida. Basándonos en la generación de historias por simulación y planificación, hemos implementado un generador de historias basado en agentes inteligentes para conseguirlo. Estos agentes utilizarán un planificador externo para saber qué es lo que tienen que hacer en cada momento, lo que dará lugar a interacciones entre los mismos. Utilizaremos estas interacciones entre agentes como contenido de las historias a generar, de forma que en cierto modo, serán estos agentes los que nos hagan parte del trabajo. Recogiendo los datos producidos por estos agentes, habremos terminado la fase de "crear", y ya sólo nos quedará la fase de "narrar". Para ello, por el momento, tendremos textos predefinidos dentro del propio código, aunque la idea inicial era utilizar un programa externo que tomase como entrada un log con las interacciones producidas. Con todo, esto, hemos logrado implementar un generador de historias básico, capaz de generar historias distintas e independientes unas de otras

Automating Abell's theory of comparative narratives

The purpose of this thesis is to demonstrate the progress that has been made towards the goal of producing a prototype computer model of Abell's Theory of Comparative Narratives, and subsequently, designing metrics to rigorously measure Abell's concept of 'closeness' of texts. The production of such a model does not simply involve the mechanical (though distinctly non-trivial) transference of Abell's theory from paper to machine; various facets of the theory are not of a sufficiently high specification for a computer model and the fulfilment of such a computer model requires attention to these areas, specifically: i) a repeatable method of comparing the structures of individual events; ii) a consistent procedure of comparing the overall structure of a pair of texts, following on from Abell's basic concept of paths of social determination. iii) metrics to demonstrate that the solutions proposed do indeed address the shortcomings of Abell's theory. In order to preserve the qualitative nature of the theory and to demonstrate its potential real-world uses, the computer model attempts to avoid complex mathematics as far as possible and to produce transparent, non-expert results