Pattern-Derived Visual Swarm Games: Multi-Scale Drone-Vision States for Interception and Sustainability Audits

arXiv:2608.23575v1 Announce Type: new Abstract: We convert drone-vision annotation streams into virtual swarm-game states without controlling physical drones. VisDrone and UAVSwarm metadata are compressed into a Bloom representation; deterministic probes produce bounded capability vectors, image-space formations, finite zero-sum payoffs, and human-readable visual overlays. The audit scales from 6imes 6 to 32imes 32 finite games and adds a repeated Markov layer with stock, fatigue, adaptation, exposure, stress, budget, data-growth, model-improvement, and entropy-budget state variables. Local screen tuning raises robust screen security from 0.526 to 0.593, and the 32imes 32 tuned screen reaches value 0.616. A field readout audit shows that fixed-pixel rasters do not improve monotonically: 128imes 128 accuracy is 67.2% and hotspot error is 0.136. The diagnosed error is shrinking image-plane bandwidth. A finite empirical-risk encoder over scale-normalized Gaussian bandwidths selects a scale-normalized encoder with lambda=1.50, reaching 77.6% accuracy at 128imes 128 and reducing joint loss by 0.185. A server-side audit checks 16{,}777{,}216 target-localization states, and a 32-round repeated-game audit over 16{,}777{,}216 trajectories selects a budget-adaptive policy with value 0.461.

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Source: arXiv cs.RO | 2026-08-26