Department of Engineering Mathematics, University of Bristol, Bristol BS8 1TW, United Kingdom.
Bristol Centre for Complexity Sciences, University of Bristol, Bristol BS8 1UB, United Kingdom.
Phys Biol. 2023 Mar 28;20(3). doi: 10.1088/1478-3975/acc4ff.
Maintaining cohesion between randomly moving agents in unbounded space is an essential functionality for many real-world applications requiring distributed multi-agent systems. We develop a bio-inspired collective movement model in 1D unbounded space to ensure such functionality. Using an internal agent belief to estimate the mesoscopic state of the system, agent motion is coupled to a dynamically self-generated social ranking variable. This coupling between social information and individual movement is exploited to induce spatial self-sorting and produces an adaptive, group-relative coordinate system that stabilises random motion in unbounded space. We investigate the state-space of the model in terms of its key control parameters and find two separate regimes for the system to attain dynamical cohesive states, including a Partial Sensing regime in which the system self-selects nearest-neighbour distances so as to ensure a near-constant mean number of sensed neighbours. Overall, our approach constitutes a novel theoretical development in models of collective movement, as it considers agents who make decisions based on internal representations of their social environment that explicitly take into account spatial variation in a dynamic internal variable.
在无界空间中维持随机移动代理之间的内聚性是许多需要分布式多代理系统的实际应用的基本功能。我们开发了一种基于生物启发的集体运动模型，用于在 1D 无界空间中确保这种功能。使用内部代理置信度来估计系统的介观状态，代理运动与动态自生成的社会排名变量耦合。利用社会信息和个体运动之间的这种耦合，诱导空间自分类，并产生自适应的、与群体相关的坐标系，从而稳定无界空间中的随机运动。我们根据模型的关键控制参数研究了其状态空间，并发现系统达到动态凝聚状态的两种分离状态，包括部分感知状态，其中系统自我选择最近邻距离，以确保感知到的邻居数量接近常数。总的来说，我们的方法是集体运动模型的一个新的理论发展，因为它考虑了基于其社会环境的内部表示做出决策的代理，这些表示明确考虑了动态内部变量中的空间变化。
