AI Agents Learning Human Decision Policies for Collaborative Situation Assessment in NORAD C2 Operations
Bibliographic record
Abstract
The modernization of Command and Control (C2) for North American Aerospace Defense (NORAD) entails supporting operators with trustworthy AI-based solutions that complement, rather than replace, human abilities. New forms of threats requires more than ever the ability to quickly derive actionable situational awareness from a set of heterogeneous sensors. In this paper, we investigate the use of Human-Automation Teaming (HAT) for achieving accurate, timely continental surveillance in the context of NORAD critical infrastructure protection. We developed a collaborative AI agent solution with awareness, anticipation and decision capabilities, augmented here with the ability to learn human decision policies for improved collaborative situation assessment. The study employs a simulated threat evaluation task to validate the effectiveness of the augmented AI-agent using a multi-model approach combining seven supervised machine learning algorithms. Results show that the policy capturing method classified threat levels with a predictive accuracy of 95% while considering three different types of targets (UAV, drone swarm, small aircraft). We conclude that integrating the policy capturing capability into a collaborative AI-agent constitutes a key step toward enabling a novel human-AI co-learning process for adjustable human-autonomy teaming.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".