Plan-based narrative generation with coordinated subplots

Despite recent progress in plan-based narrative generation, one major limitation is that systems tend to produce a single plotline whose progression entirely determines the narrative experience. However, for certain narrative genres such as serial dramas and soaps, multiple interleaved subplots are expected by the audience, as this tends to be the norm in real-world, human-authored narratives. Current narrative generation techniques have overlooked this important requirement, something which could improve the perceived quality of generated stories. To this end, we have developed a flexible plan-based approach to multiplot narrative generation, that successfully generates narratives conforming to different subplot profiles, in terms of the number of subplots interleaved and the relative time spent on each presentation. We have identified specific challenges such as: distribution of virtual characters across subplots; length of each subplot presentation; and transitioning between subplots. In this paper, we overview this approach and describe its operation in a prototype Interactive Storytelling (IS) System set in the serial drama genre. Results of experiments with the system demonstrate its usability. Furthermore, results of a user study highlight the potential of the approach, with clear user preference for presentations that feature interleaved multiple subplots

Planning Technologies for Interactive Storytelling

Since AI planning was first proposed for the task of narrative generation in interactive storytelling (IS), it has emerged as the dominant approach in this field. This chapter traces the use of planning technologies in this area, considers the core issues involved in the application of planning technologies in IS, and identifies some of the remaining challenges

CB-POCL: A Choice-Based Algorithm for Character Personality in Planning-based Narrative Generation

The quality and believability of a story can be significantly enhanced by the presence of compelling characters. Characters can be made more compelling by the portrayal of a distinguishable personality. This paper presents an algorithm that formalizes an approach previously described for the incorporation of character personality in narrative that is automatically generated. The approach is based on a computational model that operationalizes personality as behavior that results from the choices made by characters in the course of a story. This operationalization is based on the Big Five personality structure and results from behavioral psychology studies that link behavior to personality traits