A model of suspense for narrative generation

Most work on automatic generation of narratives, and more specifically suspenseful narrative, has focused on detailed domain-specific modelling of character psychology and plot structure. Recent work on the automatic learning of narrative schemas suggests an alternative approach that exploits such schemas for modelling and measuring suspense. We propose a domain-independent model for tracking suspense in a story which can be used to predict the audience’s suspense response on a sentence-by-sentence basis at the content determination stage of narrative generation. The model lends itself as the theoretical foundation for a suspense module that is compatible with alternative narrative generation theories. The proposal is evaluated by human judges’ normalised average scores correlate strongly with predicted values

Imaginative Recall with Story Intention Graphs

Intelligent storytelling systems either formalize specific narrative structures proposed by narratologists (such as Propp and Bremond), or are founded on formal representations from artificial intelligence (such as plan structures from classical planning). This disparity in underlying knowledge representations leads to a lack of common evaluation metrics across story generation systems, particularly around the creativity aspect of generators. This paper takes Skald, a reconstruction of the Minstrel creative story generation system, and maps the representation to a formal narrative representation of Story Intention Graphs (SIG) proposed by Elson et al. This mapping facilitates the opportunity to expand the creative space of stories generated through imaginative recall in Minstrel while maintaining narrative complexity. We show that there is promise in using the SIG as an intermediate representation that is useful for evaluation of story generation systems

A Software-based Knowledge Management System Using Narrative Texts

Technical and professional communicators have in recent research been challenged to make significant contributions to the field of knowledge management, and to learn or create the new technologies allowing them to do so. The purpose of this dissertation is to make such a combined theoretical and applied contribution from the context of the emerging discipline of Texts and Technology. This dissertation explores the field of knowledge management (KM), particularly its relationship to the related study of artificial intelligence (AI), and then recommends a KM software application based on the principles of narratology and narrative information exchange. The focus of knowledge is shifted from the reductive approach of data and information to a holistic approach of meaning and the way people make sense of complex events as experiences expressed in stories. Such an analysis requires a discussion of the evolution of intelligent systems and narrative theory as well as an examination of existing computerized and non-computerized storytelling systems. After a thorough discussion of these issues, an original software program that is used to collect, analyze, and distribute thematic stories within any hierarchical organization is modeled, exemplified, and explained in detail

The Significance of Transmedia Storytelling in the World of Jane Austen

Transmedia storytelling is an opportunity for developing interactive stories involving various media, which “has grown out of the increased possibilities that come with the multifaceted developments of the ever evolving Internet” (Jandl 167). This means that multiple media platforms can be utilized to convey a single story –from videos to blog posts, tweets on Twitter or status updates on Facebook. The goal of transmedia is ultimately to have many moving parts that create one cohesive storyline, and the ways in which these pieces move and work together creates a unique, immersive story experience. Taking cultural icons like Austen and adapting her in this form allows for her stories to be consumed in new ways

Hide and sneak: story generation with characters that perceive and assume

We describe the design of a perception system for the characters in the Virtual Storyteller (VST), a character-centric story generation system. Previously, these characters were omniscient; stories involving sneaking and deception could not be generated. To remedy this, we limited the characters' visual perception using simple rules. We enabled the characters to make assumptions about the story world, so they can plan toward goals in spite of incomplete knowledge. Using the distinction between the character and actor roles of agents in the VST, we can use the assumptions to steer the story plot