Three-Level AI Architecture for Autonomous UAV Swarms in SAR

This research introduces a groundbreaking three-level hierarchical learning architecture specifically designed for autonomous UAV swarms engaged in search and rescue (SAR) missions. Unlike conventional approaches that apply a single learning paradigm across all levels, this architecture integrates qualitatively distinct learning mechanisms, mirroring biological hierarchies of reflexes, skills, and reasoning. These include Hebbian neuroplasticity for individual agent adaptation, multi-agent reinforcement learning with graph neural networks and behavior trees for tactical coordination, and model-agnostic meta-learning combined with BDI (Belief-Desire-Intention) reasoning and a digital twin for strategic decision-making. The architecture is rigorously formalized through twenty-two architectural contracts, organized across six key components such as BDI, Behavior Trees, GNN, MARL, Neuroplasticity, and Meta Learning. These contracts collectively provide six classes of formal guarantees, including safety, budget correctness, optimality, liveness, starvation freedom, and inter-level consistency. A key concept introduced is "Swarm Meta Cognition," a compositional property emerging from the structured interaction of all three levels, enabling the swarm to monitor its own cognitive state and dynamically switch between cognitive strategies. The paper also defines five constructive progress functions tailored for SAR task types, bridging abstract optimization theory with concrete operational scenarios. A main integration theorem establishes that when all contracts are satisfied, the hybrid neuro-symbolic system preserves all six guarantee classes. For dynamic scenarios involving active learning, five additional contracts extend the framework with three more guarantees: cognitive resilience, graceful degradation, and monotonic meta-improvement. Theoretical analysis demonstrates that this architecture effectively addresses five fundamental limitations of existing hierarchical reinforcement learning approaches.