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Creating Hybrid Interactive Narrative Systems with LLM: From Formal Description to Evaluation — arXiv:2611.02807

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.02807
License (as archived): CC0-1.0
Archived: 2026-11-03T11:03:27.918Z
SHA-256 of the archived text: 34f1e75fb86f1a6e97aa28808da366dc107230c245cc59458d09170fad20364e
Stable URL of this page: https://metraipolis.com/documento/dc_60dcc83d79fa

How to cite

Creating Hybrid Interactive Narrative Systems with LLM: From Formal Description to Evaluation — arXiv:2611.02807 — arXiv (en), https://arxiv.org/abs/2611.02807 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_60dcc83d79fa (Metraipolis archive dc_60dcc83d79fa, sha256 34f1e75fb86f1a6e97aa28808da366dc107230c245cc59458d09170fad20364e)

BibTeX

@misc{dc60dcc83d79fa,
  title = {Creating Hybrid Interactive Narrative Systems with LLM: From Formal Description to Evaluation — arXiv:2611.02807},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.02807},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_60dcc83d79fa; sha256 34f1e75fb86f1a6e97aa28808da366dc107230c245cc59458d09170fad20364e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.02807. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL); Human-Computer Interaction (cs.HC).
Pagina del lavoro: https://arxiv.org/abs/2611.02807 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:59:45.852Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).