In this paper I present ClueGen, a murder mystery game that generates its own narrative. I discuss the genre’s suitability for combining gameplay and story using procedural generation techniques, before explaining the implementation and rationale behind ClueGen, utilizing the genre’s common structure as an opportunity to define the scope of the generation requirements. In particular I detail a novel system for telegraphing deceptive characters in procedurally generated games with text-based dialogue, by using audible cues.
Qualitative analysis of procedurally generated narratives remains a difficult hurdle for most narrative generation tools. Typical analysis involves the use of human studies, rating the quality of the generated narratives against a given set of criteria, a costly and time consuming process. In this paper we integrate a set of features within the ReGEN system which aim to ensure narrative correctness and quality. Correct generation is ensured by performing an analysis of the preconditions and postconditions of each narrative event. Narrative quality is ensured by using an existing set of formal metrics which relate quality to the structure of the narrative to guide narrative generation. This quantitative approach provides an objective means of guaranteeing quality within narrative generation.
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.
What does it mean to read a poem, story, or novel about the human condition written entirely by a computer? This is not a crazy question. Legendary author Roald Dahl had already conjured up this nightmarish scenario for authors in one of his unnerving short stories in Someone Like You (1953). It tells the tale of the Great Automatic Grammatizator, a mammoth machine able to write prizewinning novels based on the works of living authors in 15 minutes flat. Dahl died before such a machine was within the realm of possibility.
Cinematic, Ambient, Inhabitable Narrative Environments (CAINEs) are conceptual AI-driven interactive story systems combining text, audio, and visual imagery that are scalable and adaptable to a wide range of storytelling needs and interactor inputs. Conceived by at artist outside the AI community, they represent an opportunity to use AI in a nontraditional and immersive narrative fashion that relies not on the goal-based arrangement of story elements, but on the accretion and association of those elements in the minds of interactors. This paper represents the initial phase of the project’s development.