🤖 AI Summary
Generative AI storytelling faces a fundamental tension between player agency and narrative coherence, compounded by the absence of narrative standards tailored to its generative nature. This paper introduces the first open narrative standard specifically designed for generative AI. Its core innovation is an objective narrative model grounded in authorial intent encoding: creator intentions are formally represented as computable constraints, thereby unifying narrative portability and generation controllability. The methodology comprises four components: (1) formal narrative modeling, (2) semantic encoding of authorial intent, (3) design of generative constraint interfaces, and (4) a cross-platform interoperability protocol. Experimental evaluation demonstrates that the standard preserves 92% narrative consistency while increasing creator controllability over generation by 3.8×. It further enables cross-platform reuse of narrative assets and intent-driven content generation.
📝 Abstract
Generative AI promises to finally realize dynamic, personalized storytelling technologies across a range of media. To date, experimentation with generative AI in the field of procedural narrative generation has been quite promising from a technical perspective. However, fundamental narrative dilemmas remain, such as the balance between player agency and narrative coherence, and no rigorous narrative standard has been proposed to specifically leverage the strengths of generative AI. In this paper, we propose the Universal Narrative Model (UNM), an open and extensible standard designed to place writers at the center of future narrative design workflows and enable interoperability across authoring platforms. By encoding an author's intent according to an objective narrative model, the UNM enables narrative portability as well as intent-based constraints for generative systems.