[Submitted on 8 Jul 2026]
Title:Pattern-Derived Visual Swarm Games: Multi-Scale Drone-Vision States for Interception and Sustainability Audits
View a PDF of the paper titled Pattern-Derived Visual Swarm Games: Multi-Scale Drone-Vision States for Interception and Sustainability Audits, by Faruk Alpay and 1 other authors
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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 $6\times 6$ to $32\times 32$ 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 $32\times 32$ tuned screen reaches value $0.616$. A field readout audit shows that fixed-pixel rasters do not improve monotonically: $128\times 128$ 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 $128\times 128$ 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 $0.461$.
Comments: 20 pages, 8 figures. Ancillary files include reproducibility code, dataset manifests, numerical tables, remote audit outputs, and Lean/Coq formal checks
Subjects:
Robotics (cs.RO); Computer Science and Game Theory (cs.GT); Image and Video Processing (eess.IV)
MSC classes: 68T45, 68U10, 91A10, 91A15, 91A20, 94A17
ACM classes: I.2.10; I.4.8; I.5.4; I.2.11; G.3; F.2.2
Cite as: arXiv:2608.23575 [cs.RO]
(or arXiv:2608.23575v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.23575
arXiv-issued DOI via DataCite
Submission history
From: Levent Sarioglu [view email] [v1] Wed, 8 Jul 2026 02:30:15 UTC (7,763 KB)
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View a PDF of the paper titled Pattern-Derived Visual Swarm Games: Multi-Scale Drone-Vision States for Interception and Sustainability Audits, by Faruk Alpay and 1 other authors
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Ancillary-file links:
Ancillary files (details):
ANCILLARY_README.md
Dockerfile
Makefile
README.md
code/markov_game.py
code/render_tactical.py
code/sketch.py
code/swarm_game.py
data/manifests/local_downloads.json
data/manifests/source_manifest.json
dockerignore.txt
docs/dataset_sources.md
formal/README.md
formal/coq/SwarmRisk.v
formal/lean/SwarmRisk.lean
requirements.txt
results/figures/fig_bloom_game_summary.pdf
results/figures/fig_bloom_game_summary.png
results/figures/fig_field_resolution_audit.pdf
results/figures/fig_field_resolution_audit.png
results/figures/fig_field_resolution_optimization.pdf
results/figures/fig_field_resolution_optimization.png
results/figures/fig_large_field_overlay.pdf
results/figures/fig_large_field_overlay.png
results/figures/fig_markov_entropy_budget.pdf
results/figures/fig_markov_entropy_budget.png
results/figures/fig_multiscale_summary.pdf
results/figures/fig_multiscale_summary.png
results/figures/fig_tactical_before_after.pdf
results/figures/fig_tactical_before_after.png
results/figures/fig_tactical_overlay.pdf
results/figures/fig_tactical_overlay.png
results/remote_gpu/dataset_strategy_audit.csv
results/remote_gpu/dataset_strategy_audit.json
results/remote_gpu/field_resolution_audit.json
results/remote_gpu/formal_checks.json
results/remote_gpu/gpu_game_theory_sweep.json
results/remote_gpu/gpu_live_nvidia_smi_sample.txt
results/remote_gpu/markov_entropy_audit.csv
results/remote_gpu/markov_entropy_audit.json
results/remote_gpu/server_download_manifest.txt
results/summaries/experiment_summary.json
results/tables/cv_regime_benchmark.csv
results/tables/ethics_stress_audit.csv
results/tables/field_bandwidth_candidates.csv
results/tables/field_numeric_certificates.csv
results/tables/field_resolution_audit.csv
results/tables/field_resolution_optimization.csv
results/tables/field_resolution_samples.csv
results/tables/formation_payoff_matrix_seed17.csv
results/tables/literature_language_comparison.csv
results/tables/markov_policy_summary.csv
results/tables/multiscale_swarm_summary.csv
results/tables/reference_evidence_audit.csv
results/tables/seed17_tactical_audit.csv
results/tables/seed17_tactical_summary.csv
results/tables/source_ablation_seed17.csv
results/tables/swarm_game_summary.csv
results/tables/visual_regime_samples.csv
scripts/materialize_experiments.py
server/bootstrap.sh
server/dataset_strategy_audit.py
server/download_datasets.sh
server/field_resolution_audit.py
server/gpu_game_theory_sweep.py
server/markov_entropy_audit.py
server/run_dataset_strategy_audit.sh
server/run_field_resolution_audit.sh
server/run_formal_checks.sh
server/run_gpu_game_theory_sweep.sh
server/run_markov_entropy_audit.sh
(66 additional files not shown)
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