通过演化神经网络揭示最小交互下群体行为的涌现机制
Evolving Neural Networks Reveal Emergent Collective Behavior from Minimal Agent Interactions
- 用演化算法优化神经网络控制多智能体，研究复杂度与行为模式关系
- 非线性越强，群体行为越复杂；适度噪声等环境条件促进复杂行为演化
- 为自组织智能系统设计提供可调控的协同演化路径，适合群体智能研究者
理解多智能体系统中涌现行为的机制对群体机器人和人工智能领域至关重要。本研究通过量化与定性分析，探究神经网络在动态环境中演化控制智能体行为的过程，重点关注网络复杂度与集体行为模式之间的关系。结果表明，网络非线性程度与涌现行为复杂性正相关：简单行为（如车道形成、层流）对应较线性的网络操作，而复杂行为（如集群、群体飞行）则表现出高度非线性的神经处理。此外，适度噪声、更广视野范围及较低智能体密度等环境参数，能促进非线性网络的演化，驱动更丰富、更复杂的集体行为。研究强调了调整演化条件以诱导期望行为的重要性，为自主群体的协调优化提供了新路径。成果深化了对神经机制如何影响集体动力学的理解，对智能自组织系统的设计具有重要意义。
原文摘要 · Abstract (English)
Understanding the mechanisms behind emergent behaviors in multi-agent systems is critical for advancing fields such as swarm robotics and artificial intelligence. In this study, we investigate how neural networks evolve to control agents' behavior in a dynamic environment, focusing on the relationship between the network's complexity and collective behavior patterns. By performing quantitative and qualitative analyses, we demonstrate that the degree of network non-linearity correlates with the complexity of emergent behaviors. Simpler behaviors, such as lane formation and laminar flow, are characterized by more linear network operations, while complex behaviors like swarming and flocking show highly non-linear neural processing. Moreover, specific environmental parameters, such as moderate noise, broader field of view, and lower agent density, promote the evolution of non-linear networks that drive richer, more intricate collective behaviors. These results highlight the importance of tuning evolutionary conditions to induce desired behaviors in multi-agent systems, offering new pathways for optimizing coordination in autonomous swarms. Our findings contribute to a deeper understanding of how neural mechanisms influence collective dynamics, with implications for the design of intelligent, self-organizing systems.
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