Narrative AI needs to model more than just action. In this paper, we investigate the Belief-Desire-Intention (BDI) agent architecture to allow plots to be modelled in terms of character motivation. This allows authors to focus on elements of the character model which are highly relevant to plot. We describe an extended implementation of the ConGolog agent programming language which includes BDI syntax and semantics. Using this language, we provide an example of how plot could be advantageously modelled in terms of character motivation.
My thesis aims at conceptualizing and implementing a computational model of narrative generation that is informed by narratological theory as well as cognitive multi-agent simulation models. It approaches this problem by taking a mimetic stance towards fictional characters and investigates how narrative phenomena related to characters can be computationally recreated from a deep character model grounded in multi agent systems. Based on such a conceptualization of narrative it explores how the generation of plot can be controlled, and how the quality of the resulting plot can be evaluated, in dependence of fictional characters. By that it contributes to research on computational creativity by implementing an evaluative storytelling system, and to narratology by proposing a generative narrative theory based on several post-structuralist descriptive theories.
Kazakova, Vera A. (University of Central Florida) | Hastings, Lauren (University of Central Florida) | Posadas, Andres (University of Central Florida) | Gonzalez, Lucas C. (University of Central Florida) | Knauf, Rainer ( Technische Universität Ilmenau ) | Jantke, Klaus P. (ADICOM Software GmbH) | Gonzalez, Avelino J. (University of Central Florida)
In this work we present fAIble: a novel graph-based modular storytelling framework. fAIble is centered around a graph database, incorporates invariable elements of folktale structure, while accounting for thoughts and actions. Action outcomes are a product of probabilistic story generation. Probabilities are based on elements of common sense, invariable elements of folktale structure, high-level character roles, and a wide variety of other variables (e.g. characters' physical and psychological traits, context-based likelihood of encountering specific items and characters, etc.). A prototype implementation is tested through an anonymous questionnaire. Results demonstrate the ability of graph-based cognition to produce coherent story prototypes with sensible character actions, while maintaining output variability.
Narrative is one of the fundamental modes of creative expression. Creativity plays a central role in the effective design of film, novels, oral stories, computer games and most other narrative media. Beyond the creation of narratives intended to entertain, narrative is used as a powerful mode of communication in a range of other contexts. Narrative and its creative design plays a role in education and training (Gee, 2003), the communication of scientific ideas (Hoffman, 2005), characterizations of business practices (Thomas, 1999), communicating cultural heritage (Collins et al, 2003) and other contexts. There is growing awareness of the foundational role of narrative in our understanding of not only fictional worlds (Gerrig, 1993) but also the real world around us (Bruner 1991).
The aim of this paper is to revisit the fundamental requirements for bulding computational models for Interactive Narrative. We express the need for broader computational models of narrative and underline the fundamental difference between models for story generation and models for Interactive Narrative. Research directions are finally sketched to move towards dedicated computational models for Interactive Narrative.