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Position: Profiling Game Worlds by Transition Complexity — arXiv:2608.18079

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.18079
License (as archived): CC0-1.0
Archived: 2026-10-18T13:44:14.636Z
SHA-256 of the archived text: f7fbc67198469f1f8c184b861af3c200f22036bfb01454f7c3eb1a1ebc74bb04
Stable URL of this page: https://metraipolis.com/documento/dc_64dbff44fe11

How to cite

Position: Profiling Game Worlds by Transition Complexity — arXiv:2608.18079 — arXiv (en), https://arxiv.org/abs/2608.18079 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_64dbff44fe11 (Metraipolis archive dc_64dbff44fe11, sha256 f7fbc67198469f1f8c184b861af3c200f22036bfb01454f7c3eb1a1ebc74bb04)

BibTeX

@misc{dc64dbff44fe11,
  title = {Position: Profiling Game Worlds by Transition Complexity — arXiv:2608.18079},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.18079},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_64dbff44fe11; sha256 f7fbc67198469f1f8c184b861af3c200f22036bfb01454f7c3eb1a1ebc74bb04},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.18079. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Game world modeling (GWM) and reinforcement learning (RL) are often confounded because research papers rarely quantify how difficult the underlying transition prediction problem is at the declared interface (pixels/tokens/latents with finite history). We propose the Transition Complexity Profile (TCP): a small, reproducible set of metrics that characterizes an environment's (or gameplay dataset's) induced transition kernel by (i) intrinsic one-step branching, (ii) interaction-induced uncertainty and opponent influence when observable, and (iii) temporal/spatial dependency span via standardized probe curves. TCP is reported with an explicit reference distribution, protocol stochasticity, and a versioned measurement budget (sampling/resampling and fixed probe compute), enabling comparable numbers across benchmarks. We outline how common game families and modern "neural game engine" domains populate this landscape and call for TCP to become standard benchmark metadata and a required statistic in GWM and RL papers.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-20 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.18079 — 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 2026-10-18T13:41:17.832Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-20?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).