StoryVerse: Towards Co-authoring Dynamic Plot with LLM-based Character Simulation via Narrative Planning
Yi Wang, Qian Zhou, David Ledo

TL;DR
StoryVerse introduces a novel workflow for dynamic game plot creation that combines authorial intent with LLM-driven character simulation, enabling adaptive and co-created narratives through a structured narrative planning process.
Contribution
The paper presents a new authorial structure called 'abstract acts' and a narrative planning process that mediates between writer input and emergent LLM-driven character behaviors.
Findings
Creates adaptive 'living stories' that respond to game states
Enables high-level authorial control over emergent narratives
Demonstrates versatility across different game environments
Abstract
Automated plot generation for games enhances the player's experience by providing rich and immersive narrative experience that adapts to the player's actions. Traditional approaches adopt a symbolic narrative planning method which limits the scale and complexity of the generated plot by requiring extensive knowledge engineering work. Recent advancements use Large Language Models (LLMs) to drive the behavior of virtual characters, allowing plots to emerge from interactions between characters and their environments. However, the emergent nature of such decentralized plot generation makes it difficult for authors to direct plot progression. We propose a novel plot creation workflow that mediates between a writer's authorial intent and the emergent behaviors from LLM-driven character simulation, through a novel authorial structure called "abstract acts". The writers define high-level plot…
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