Intelligent Three Level Learning Architecture for Autonomous UAV Swarms in Search and Rescue
Тип публікації :
Препринт
Дата випуску :
9 квітня 2026 р.
Автор(и) :
Oleksii Bychkov
eKNUTSHIR URL :
Журнал :
arXiv (Cornell University)
Цитування :
[APA 7] Oleksii, B. (2026). Intelligent Three Level Learning Architecture for Autonomous UAV Swarms in Search and Rescue. arXiv (Cornell University),. https://ir.library.knu.ua/handle/15071834/34318
[ДСТУ] Oleksii B. Intelligent Three Level Learning Architecture for Autonomous UAV Swarms in Search and Rescue. arXiv (Cornell University). 2026. URL: https://ir.library.knu.ua/handle/15071834/34318 (дата звернення: 11.09.2026).
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 such as 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 with BDI reasoning and a digital twin for strategic decision making. The architecture is formalized through twenty two architectural contracts organized across six components such as BDI, Behavior Trees, GNN, MARL, Neuroplasticity, Meta Learning that collectively provide six classes of formal guarantees such as 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 such as cognitive resilience, graceful degradation, and monotonic meta improvement. Theoretical analysis demonstrates that the architecture addresses five fundamental limitations of existing hierarchical RL approaches.
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