BANE is an AI-powered war game debrief and training system that analyzes mission gameplay and user perception data to deliver personalized, Weapons School-grade feedback for Airmen across all experience levels
- Less than 5 % of the USAF are rated officers; yet every Airman must grasp the "family business" of air-power.
- Great-Power Competition demands rapid, scalable tactical learning across the force.
- Traditional human-only debrief pipelines can’t meet demand—BANE scales Weapons School expertise to everyone.
| Step | Action |
|---|---|
| 1 › | Drag & Drop Scenario (PDF) – Upload mission rules, assets, threats |
| 2 › | Fly / Simulate Mission – Solo or instructor-led playthrough |
| 3 › | Upload Log (JSON) – Drop generated gameplay log |
| 4 › | Instant AI Debrief – Timeline, causal analysis, graded focus points |
| 5 › | Perception Analysis – Optional eye-tracking stream pinpoints what the trainee saw vs. missed |
Scenario: INDOPACOM strike-package escort versus adversary naval group.
https://drive.google.com/file/d/1SiPJ3YWXkQwKLGsfjZ4HTV-lc09_y5gc/view?usp=sharing
https://cerebralvalley.pixieset.com/nationalsecurityhackathon/demovideos/
| Capability | Description |
|---|---|
| Event Extraction | Parses mission logs into an ordered timeline of tactical events |
| Causal Inference | Links actions to outcomes using doctrinal rules and LLM reasoning |
| Eye-Tracking Fusion | Separates decision errors from perception gaps |
- RL Curriculum Learning – Self-play data (à la KataGo) to evolve AI adversaries & coaching agents
- Deep Causal Graphs – Multi-layer reasoning across mission, perception, and comms
- Personalized Mini-Games – Micro-drills tuned to individual weaknesses
- Additional Sensors – Heart-rate, G-force, EEG for cognitive-load analysis
# Requires Python 3.13
python3 --version
python3 -m venv myenv
source myenv/bin/activate
pip install -r backend/requirements.txt
# Launch back-end
cd backend
export ANTHROPIC_API_KEY=Your Key
uvicorn main:app --reload
# Launch local web front-end
npm install
npm run devOpen browser and go to http://localhost:5173/
Drag in an instructor PDF (Wargame Scenario Considerations.pdf)
Fly the mission, and drop mission log JSON (scenario1.json) for instant debrief.
“BANE turns mission & perception data into Weapons School-grade debriefs today—and will fuel reinforcement-learning curricula for even smarter training tomorrow.”