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 outlines
that are later transformed into concrete character action sequences via an
LLM-based narrative planning process, based on the game world state. The
process creates "living stories" that dynamically adapt to various game world
states, resulting in narratives co-created by the author, character simulation,
and player. We present StoryVerse as a proof-of-concept system to demonstrate
this plot creation workflow. We showcase the versatility of our approach with
examples in different stories and game environments.

自动生成游戏情节提升了玩家的体验，通过提供丰富而沉浸式的叙述体验，适应玩家的操作。我们提出了一种新的情节创作工作流程，通过使用大型语言模型驱动虚拟角色的行为，导入作者的意图并创造出根据游戏世界状态动态适应的故事，最终实现了作者、角色模拟和玩家共同创作的故事。

StoryVerse：基于 LMM 的人物模拟通过叙事规划实现共同编写动态情节

StoryVerse: Towards Co-authoring Dynamic Plot with LLM-based Character  Simulation via Narrative Planning

Automated plot generation is the challenge of generating a sequence of events
that will be perceived by readers as the plot of a coherent story. Traditional
symbolic planners plan a story from a goal state and guarantee logical causal
plot coherence but rely on a library of hand-crafted actions with their
preconditions and effects. This closed world setting limits the length and
diversity of what symbolic planners can generate. On the other hand,
pre-trained neural language models can generate stories with great diversity,
while being generally incapable of ending a story in a specified manner and can
have trouble maintaining coherence. In this paper, we present an approach to
story plot generation that unifies causal planning with neural language models.
We propose to use commonsense knowledge extracted from large language models to
recursively expand a story plot in a backward chaining fashion. Specifically,
our system infers the preconditions for events in the story and then events
that will cause those conditions to become true. We performed automatic
evaluation to measure narrative coherence as indicated by the ability to answer
questions about whether different events in the story are causally related to
other events. Results indicate that our proposed method produces more coherent
plotlines than several strong baselines.

本文提出了一种将因果规划与神经语言模型统一起来的故事情节生成方法，该方法利用从大型语言模型中提取的常识知识以递归反向链接的方式扩展故事情节，并通过自动化评估证明相对于其他强基线方法它能生成更具连贯性的情节。