Metraipolis › archive › dc_83eff90f8a0c

Energy as a Concealable State in Adversarial UAV Patrolling: Formulation, an Energy-Security Threshold, and the Limits of Self-Play — arXiv:2608.26518

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.26518
License (as archived): CC0-1.0
Archived: 2026-10-17T22:20:49.626Z
SHA-256 of the archived text: cbae037afec2a26671555a495cf8c7870df69f8045f428775571b4ba0b4f7980
Stable URL of this page: https://metraipolis.com/documento/dc_83eff90f8a0c

How to cite

Energy as a Concealable State in Adversarial UAV Patrolling: Formulation, an Energy-Security Threshold, and the Limits of Self-Play — arXiv:2608.26518 — arXiv (en), https://arxiv.org/abs/2608.26518 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_83eff90f8a0c (Metraipolis archive dc_83eff90f8a0c, sha256 cbae037afec2a26671555a495cf8c7870df69f8045f428775571b4ba0b4f7980)

BibTeX

@misc{dc83eff90f8a0c,
  title = {Energy as a Concealable State in Adversarial UAV Patrolling: Formulation, an Energy-Security Threshold, and the Limits of Self-Play — arXiv:2608.26518},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.26518},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_83eff90f8a0c; sha256 cbae037afec2a26671555a495cf8c7870df69f8045f428775571b4ba0b4f7980},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.26518. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We study energy-constrained adversarial patrolling on a graph, in which a battery-limited UAV defends a cluster of high-value targets against a strategic attacker who chooses when and where to strike. Unlike prior adversarial patrolling, the patroller must periodically return to a base to recharge; unlike prior energy-aware patrolling, it faces a self-interested adversary. Our central observation is that the remaining energy is a hidden state: the attacker never observes the battery directly, but observes the patroller's trajectory and can infer when a recharge excursion, and thus a vulnerability window, is imminent. We formalize the interaction as a zero-sum partially observable stochastic game and report a negative result on the solver side: neither independent deep Q-learning nor Neural Fictitious Self-Play reaches a stable equilibrium at this scale; each improves transiently and then collapses, with the co-trained thwart rate falling from about 0.25 to about 0.09 over training. Using a structural analysis independent of the learning dynamics, we show that achievable security rises monotonically with the energy budget, from zero below a threshold to about 0.7 when the budget is ample, establishing the energy budget as the primary determinant of defensibility. We set out the program the model is built to answer: whether an inference-capable attacker concentrates its successful strikes in the recharge window, and whether the defender can learn deceptive recharge timing to keep that window closed.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-08-28 in eess.SY).
Categorie arXiv: Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2608.26518 — 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 2026-10-17T22:17:01.645Z dalla pagina https://arxiv.org/catchup/eess.SY/2026-08-28?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).