Adversarial Observation Perturbations and Policy Inference (AOI-GBE)

Establish a high‑quality, labeled interaction log repository and baseline detection metrics.

Steps

• Define Observation Taxonomy(3 wks)
  Catalog nominal, noisy, spoofed, and semantic perturbation types across target modalities.
• Deploy Sensor Suite & Logging(4 wks)
  Instrument a small UAV swarm (5 agents) with multi‑modal sensors and secure telemetry for 2‑week data capture.
• Generate Synthetic Adversarial Logs(3 wks)
  Use rule‑based and LLM‑based generators to create controlled perturbation scenarios for training.
• Baseline Detection Benchmark(2 wks)
  Implement simple anomaly detectors (entropy, statistical) to establish performance baselines.

Milestones

Risks

• Insufficient diversity in synthetic perturbations leading to over‑fitting
• Hardware failures causing data gaps

Build and validate the conditional GAN observation model, Bayesian policy inference layer, and cooperative resilience module.

Steps

• CC‑GAN Architecture Design(4 wks)
  Define generator/discriminator networks with temporal GRU and multimodal conditioning heads.
• Offline Training & Stability(8 wks)
  Train CC‑GAN on mixed nominal/adversarial data, apply physics‑based regularizers and differential privacy.
• Bayesian Policy Inference Engine(6 wks)
  Implement hierarchical Bayesian inference with amortized variational posterior over policies.
• Cooperative Resilience Layer(4 wks)
  Embed entropy monitoring and local recovery policy triggers into the policy prior.
• Explainable Inference Traces(4 wks)
  Add latent‑space saliency and counterfactual modules to the inference pipeline.

Milestones

Risks

• GAN mode collapse or unrealistic reconstructions
• Computational bottleneck in Bayesian marginalization
• Entropy threshold mis‑tuning leading to false recoveries

Dependencies

• Phase 1 Dataset Ready

Generate diverse semantic adversarial scenarios and enable online adaptation of the generative model.

Steps

• LLM‑AC Pipeline Construction(4 wks)
  Integrate LLM‑TOC with attacker‑target‑judge loop to produce high‑impact perturbations.
• Curriculum Evaluation(3 wks)
  Quantify regret increase and policy brittleness across generated scenarios.
• Meta‑Learner Design(4 wks)
  Implement MAML‑style initialization for CC‑GAN and define few‑shot fine‑tuning protocol.
• Online Drift Detection(3 wks)
  Deploy statistical drift detectors on observation streams to trigger meta‑updates.

Milestones

Risks

• LLM hallucinations producing unrealistic scenarios
• Meta‑learning causing catastrophic forgetting
• Edge compute constraints limiting update speed

Dependencies

• Phase 2 CC‑GAN & BPI ready

Combine all modules into a cohesive, fault‑tolerant multi‑agent control stack.

Steps

• Centralized Training, Decentralized Execution (CTDE) Setup(3 wks)
  Configure MAPPO‑style training with shared critic and local actors.
• Federated Learning & Privacy Layer(3 wks)
  Add secure aggregation and differential privacy to gradient sharing.
• Quantum‑Enhanced Digital Twin Prototype(4 wks)
  Prototype entangled register mapping for observation verification.
• End‑to‑End Latency Benchmark(2 wks)
  Measure inference, adaptation, and recovery latency across the stack.

Milestones

Risks

• Integration bugs causing policy divergence
• Privacy layer overhead degrading real‑time performance
• Quantum prototype not scalable to fleet size

Dependencies

• Phase 3 LLM‑AC & ML‑ITA ready

Validate the full AOI‑GBE system in a realistic operational environment and prepare for commercial deployment.

Steps

• Field Pilot Deployment(6 wks)
  Deploy 10 UAVs in a contested airspace simulation for 4 weeks.
• Human‑in‑the‑Loop Evaluation(3 wks)
  Operators use EIT saliency maps to diagnose failures and provide feedback.
• Operational Metrics Collection(3 wks)
  Track mission success, recovery latency, and false‑positive rates.
• Production Packaging(4 wks)
  Containerize models, build CI/CD for OTA updates, and document compliance.

Milestones

Risks

• Unanticipated environmental interference
• Operator overload from complex explanations
• Regulatory barriers to deployment

Dependencies

• Phase 4 Integration complete