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On Learning Optimal Corners in Orthogonal Partially Observable Cooperative Guard Art Galleries — arXiv:2610.06777

Source: arXiv
Original: https://arxiv.org/abs/2610.06777
License (as archived): CC0-1.0
Archived: 2026-10-06T05:23:55.810Z
SHA-256 of the archived text: 9b54bbbfeb3b6d2bbc1beb7d85f9f5a4b2481effcf4ad56e7b15a84e0858fa2e
Stable URL of this page: https://metraipolis.com/documento/dc_ca4994776166

How to cite

On Learning Optimal Corners in Orthogonal Partially Observable Cooperative Guard Art Galleries — arXiv:2610.06777 — arXiv, https://arxiv.org/abs/2610.06777 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ca4994776166 (Metraipolis archive dc_ca4994776166, sha256 9b54bbbfeb3b6d2bbc1beb7d85f9f5a4b2481effcf4ad56e7b15a84e0858fa2e)

BibTeX

@misc{dcca4994776166,
  title = {On Learning Optimal Corners in Orthogonal Partially Observable Cooperative Guard Art Galleries — arXiv:2610.06777},
  howpublished = {arXiv},
  url = {https://arxiv.org/abs/2610.06777},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ca4994776166; sha256 9b54bbbfeb3b6d2bbc1beb7d85f9f5a4b2481effcf4ad56e7b15a84e0858fa2e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv, https://arxiv.org/abs/2610.06777. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The CADENCE algorithm solves the Partially Observable Cooperative Guard Art Gallery Problem (POCGAGP) with formal coverage and connectivity guarantees, but leaves unspecified which valid corner each agent should be deployed to, a choice that strongly affects efficiency. We introduce two learned corner-selection heuristics that preserve these guarantees: a CNN scoring candidates on a grid encoding, and a GATv2 network trained with Deep Q-Learning (DQN) on a visibility graph. Across 7,500 runs on random orthogonal environments (50x50 to 250x250), our heuristics outperform baseline CADENCE in both steps to full coverage and peak agent count, with gains growing with scale, and improve on Incremental Self-Deployment (ISDA) baselines in agent utilization while providing guarantees ISDA lacks. Learned corner selection thus improves CADENCE in speed and agent utilization at no cost to its formal properties.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.MA, nuovi lavori del giorno).
Categorie arXiv: Multiagent Systems (cs.MA) ; Machine Learning (cs.LG); Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2610.06777 — 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 2026-10-06T05:23:50.216Z dalla pagina https://arxiv.org/list/cs.MA/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).