Abstract
This paper presents a novel three-level hierarchical learning architecture for autonomous UAV swarms performing search and rescue operations. Unlike conventional approaches that apply a single learning paradigm across all hierarchy levels, the proposed architecture integrates three qualitatively different learning mechanisms corresponding to the biological hierarchy of reflexes, skills, and reasoning:
- Individual Adaptation: Hebbian neuroplasticity.
- Tactical Coordination: Multi-agent reinforcement learning with graph neural networks and behavior trees.
- Strategic Decision Making: Model-agnostic meta-learning with BDI reasoning and a digital twin.
The architecture is formalized through twenty-two architectural contracts organized across six components, such as BDI, Behavior Trees, GNN, MARL, Neuroplasticity, and Meta Learning, which collectively provide six classes of formal guarantees, including safety, budget correctness, optimality, liveness, starvation freedom, and inter-level consistency. We introduce Swarm Meta Cognition as a compositional property arising from the structured interaction of all three levels, enabling the swarm to monitor its own cognitive state and switch between cognitive strategies.
Five constructive progress functions for SAR task types bridge the gap between abstract optimization theory and concrete operational scenarios. The main integration theorem establishes that when all contracts are satisfied, the hybrid neuro-symbolic system preserves all six guarantee classes. For the dynamic case with active learning, five new contracts extend the framework with three additional guarantees: cognitive resilience, graceful degradation, and monotonic meta improvement. Theoretical analysis demonstrates that the architecture addresses five fundamental limitations of existing hierarchical RL approaches.
Blogger's Review: This study's proposed three-level learning architecture is not only theoretically innovative but also provides robust guarantees for autonomous decision-making in UAV swarms through specific contract mechanisms. This hierarchical learning approach, inspired by biological systems, can better adapt to complex search and rescue tasks, showcasing strong practical application potential.