AI 中文总结
本文针对无人机群搜索和救援提出智能三级学习架构，集成三种学习机制，通过架构契约形式化，引入群体元认知，有进展函数和主整合定理，解决了现有分层强化学习方法的五个基本限制。
AI 中文摘要
本文提出了一种用于自主无人机群执行搜索和救援行动的新型三级分层学习架构。与传统方法不同，该架构集成了三种性质不同的学习机制，对应生物层次的反射、技能和推理。架构通过22个架构契约形式化，跨越六个组件，提供六类形式保证。引入群体元认知，五个建设性进展函数弥合理论与实际场景差距。主整合定理表明满足契约时混合神经符号系统保留所有保证类。动态情况下五个新契约扩展框架并增加保证。理论分析表明该架构解决了现有分层强化学习方法的五个基本限制。
英文摘要
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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