AI Agents Learning Human Decision Policies for Collaborative Situation Assessment in NORAD C2 Operations

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.