WhatELSE: Shaping Narrative Spaces at Configurable Level of Abstraction for AI-bridged Interactive Storytelling

要旨

Generative AI significantly enhances player agency in interactive narratives (IN) by enabling just-in-time content generation that adapts to player actions. While delegating generation to AI makes IN more interactive, it becomes challenging for authors to control the space of possible narratives - within which the final story experienced by the player emerges from their interaction with AI. In this paper, we present WhatELSE, an AI-bridged IN authoring system that creates narrative possibility spaces from example stories. WhatELSE provides three views (narrative pivot, outline, and variants) to help authors understand the narrative space and corresponding tools leveraging linguistic abstraction to control the boundaries of the narrative space. Taking innovative LLM-based narrative planning approaches, WhatELSE further unfolds the narrative space into executable game events. Through a user study (N=12) and technical evaluations, we found that WhatELSE enables authors to perceive and edit the narrative space and generates engaging interactive narratives at play-time.

著者
Zhuoran Lu
Autodesk Research, Toronto, Ontario, Canada
Qian Zhou
Autodesk Research, Toronto, Ontario, Canada
Yi Wang
Autodesk Research, San Francisco, California, United States
DOI

10.1145/3706598.3713363

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713363

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Digital Storytelling

G304
7 件の発表
2025-04-29 01:20:00
2025-04-29 02:50:00
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