AI 中文总结
研究认知群体智能体中自愈协调，基于非马尔可夫集体运动模型，用布洛赫型快慢架构，每个智能体有感知寄存器与慢调节状态耦合，通过多种子消融实验评估，结果显示该架构对自愈有主要功能影响。
AI 中文摘要
反应性聚集模型通常将当前局部观测直接映射到运动上，使得内部感知状态在干扰后恢复的塑造空间有限。基于基于自我调节感知动力学的非马尔可夫集体运动模型，我们探究布洛赫型快慢架构是否能支持认知群体智能体中的自愈协调。每个智能体携带一个与慢调节状态耦合的有界布洛赫型感知寄存器。慢状态不被视为独立的记忆存储，感知记忆在封闭的快慢回路中用于表示依赖历史的线索解析。布洛赫更新是内部感知替代的保正有效动力学。我们在具有有限速度、有界转向、避撞、高度调节和固定迁徙驱动的非周期性、障碍物丰富的无人机迁徙任务中评估该架构。多种子消融实验使用恢复时间、最大集群恢复、极性顺序、局部相干性、碰撞风险和路径效率，将完整的快慢架构与无记忆和部分反馈基线进行比较。结果表明，主要功能影响在于自愈：在障碍物导致碎片化后，封闭的快慢回路加速空间连通性的恢复，而未耦合的慢轨迹表现得像无记忆控制器。
英文摘要
Reactive flocking models usually map current local observations directly to motion, leaving limited room for internal perceptual state to shape recovery after disruption. Building on a non-Markovian collective-motion model based on self-regulated perceptual dynamics, we ask whether the Bloch-type slow-fast architecture can support self-healing coordination in cognitive swarm agents. Each agent carries a bounded Bloch-type perceptual register coupled to a slow regulatory state. The slow state is not treated as a standalone memory store; here, perceptual memory is used operationally to denote history-dependent cue resolution within the closed slow-fast loop. The Bloch update is a positivity-preserving effective dynamics for internal perceptual alternatives, not a microscopic quantum claim. We evaluate the architecture in a non-periodic, obstacle-rich drone migration task with finite speed, bounded turning, collision avoidance, altitude regulation, and a fixed migratory drive. Multi-seed ablations compare the full slow-fast architecture with memoryless and partial-feedback baselines using recovery time, largest-cluster restoration, polar order, local coherence, collision risk, and path efficiency. Results show that the main functional impact is on self-healing: after obstacle-induced fragmentation, the closed slow-fast loop accelerates restoration of spatial connectedness, whereas an uncoupled slow trace behaves like a memoryless controller.
CommentsUnder review at IEEE Transactions on Cognitive and Developmental Systems
