arXiv cs.CL· Jules Clerc (Heudiasyc), Domitile Lourdeaux (Heudiasyc), Mohamed Sallak (Heudiasyc), Marc Ravaine·· 11 小时前AI 评分29
用 LLM 构建混合交互叙事系统：从形式化描述到评估
Creating Hybrid Interactive Narrative Systems with LLM: From Formal Description to Evaluation
AI 导读
研究者提出一个基于 LLM 的交互叙事平台，将 Experience Manager（EM）设计为可互换模块，运行在由事实、操作和目标构成的共享符号世界模型上，使每次会话都能以统一的状态转移表示作为规划进行分析。
正文 · 原文
Abstract:Interactive narratives let users shape a story while an Experience Manager (EM) keeps it coherent, balancing player freedom against authorial control. Large Language Models now enable a new generation of hybrid EMs, which combine run-time generation from free-text input with symbolic narrative control. However, their heterogeneity makes them hard to compare: A manager's behaviour is generally tied to the unique and specific scenario in which it operates, and much of its decision-making takes place outside of any explicit and verifiable structure. We address this with an LLM-based narrative platform whose managers are interchangeable modules operating over a shared symbolic world model of facts, operations, and goals. Because every applied operation carries explicit preconditions and effects, each session can be analysed as a plan through a common state-transition representation and evaluated, independently of how the manager works internally, with shared indicators of four properties: player agency, EM controllability, author intent, and robustness. We implement three contrasting managers, based respectively on open generation, Monte Carlo repair, and autonomous characters, and compare them through functional analysis on an interactive Little Red Riding Hood scenario. The results show that no manager dominates on every property, giving authors and EM designers a concrete basis to select a manager according to their priorities.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2611.02807 [cs.AI] |
| (or arXiv:2611.02807v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.02807 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | 38th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2026), IEEE Computer Society, Nov 2026, Boca Raton, United States |
Submission history
From: Jules CLERC [view email] [via CCSD proxy]
[v1]
Mon, 2 Nov 2026 09:15:01 UTC (227 KB)
来源：arXiv cs.CL · arxiv.org