三维层级无人机群中的有界耦合AI学习动态
Bounded Coupled AI Learning Dynamics in Tri-Hierarchical Drone Swarms
AI总结：
本文研究了三维层级无人机群学习系统，探讨了不同时间尺度下的学习机制耦合动态，证明了误差有界性、表示漂移有界性、元学习兼容性和误差非累积性等理论结果。
摘要：
现代自主多智能体系统结合在不同时间尺度上运行的异质学习机制。一个开放性问题仍然是：能否正式保证此类机制的耦合动态保持在可接受的操作范围内？本文研究了一种三维层级群学习系统，其中三个机制同时作用：（1）局部赫本学习（Hebbian）在线学习在个体智能体层面（快速时间尺度，10-100毫秒）；（2）多智能体强化学习（MARL）用于战术群体协调（中等时间尺度，1-10秒）；（3）元学习（MAML）用于战略适应（慢时间尺度，10-100秒）。四个结果被建立。有界总误差定理表明，在学习速率的契约约束、层级映射的Lipschitz连续性和权重稳定化条件下，总次优度允许在时间上一致的分量上界。有界表示漂移定理给出了赫本更新如何影响协调层面嵌入的最坏情况估计。元层面兼容性定理提供了战略适应保持低层不变量的充分条件。非累积定理证明误差不会随时间无限制增长。
英文摘要：
Modern autonomous multi-agent systems combine heterogeneous learning mechanisms operating at different timescales. An open question remains: can one formally guarantee that coupled dynamics of such mechanisms stay within the admissible operational regime? This paper studies a tri-hierarchical swarm learning system where three mechanisms act simultaneously: (1) local Hebbian online learning at individual agent level (fast timescale, 10-100 ms); (2) multi-agent reinforcement learning (MARL) for tactical group coordination (medium timescale, 1-10 s); (3) meta-learning (MAML) for strategic adaptation (slow timescale, 10-100 s). Four results are established. The Bounded Total Error Theorem shows that under contractual constraints on learning rates, Lipschitz continuity of inter-level mappings, and weight stabilization, total suboptimality admits a component-wise upper bound uniform in time. The Bounded Representation Drift Theorem gives a worst-case estimate of how Hebbian updates affect coordination-level embeddings during one MARL cycle. The Meta-Level Compatibility Theorem provides sufficient conditions under which strategic adaptation preserves lower-level invariants. The Non-Accumulation Theorem proves that error does not grow unboundedly over time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
