Three-Level Learning Architecture for Autonomous UAV Swarms in Search and Rescue

It is a hierarchical learning framework combining reflexive learning (Hebbian neuroplasticity), skillful learning (multi-agent reinforcement learning with graph neural networks), and reasoning learning (meta-learning with BDI reasoning and digital twin) to improve UAV swarm performance in search and rescue.

It overcomes five fundamental limitations of traditional HRL by integrating different learning mechanisms per level and providing formal guarantees for safety and consistency, making it more reliable in dynamic environments.

Yes, it can be used for search and rescue in desert or urban areas, or for monitoring critical infrastructure like oil and gas pipelines, enhancing operational efficiency and reducing human risk.