Instituto Superior Técnico (IST), Lisbon, Portugal.
Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, Portugal.
PLoS One. 2019 Oct 29;14(10):e0224376. doi: 10.1371/journal.pone.0224376. eCollection 2019.
In large scale systems of embodied agents, such as robot swarms, the ability to flock is essential in many tasks. However, the conditions necessary to artificially evolve self-organised flocking behaviours remain unknown. In this paper, we study and demonstrate how evolutionary techniques can be used to synthesise flocking behaviours, in particular, how fitness functions should be designed to evolve high-performing controllers. We start by considering Reynolds' seminal work on flocking, the boids model, and design three components of a fitness function that are directly based on his three local rules to enforce local separation, cohesion and alignment. Results show that embedding Reynolds' rules in the fitness function can lead to the successful evolution of flocking behaviours. However, only local, fragmented flocking behaviours tend to evolve when fitness scores are based on the individuals' conformity to Reynolds' rules. We therefore modify the components of the fitness function so that they consider the entire group of agents simultaneously, and find that the resulting behaviours lead to global flocking. Furthermore, the results show that alignment need not be explicitly rewarded to successfully evolve flocking. Our study thus represents a significant step towards the use of evolutionary techniques to synthesise collective behaviours for tasks in which embodied agents need to move as a single, cohesive group.
在大规模的实体代理系统中，如机器人群，集群能力在许多任务中是必不可少的。然而，人为地进化出自组织的集群行为所需的条件仍然未知。在本文中，我们研究并展示了进化技术如何用于合成集群行为，特别是如何设计适应度函数来进化高性能控制器。我们首先考虑了 Reynolds 关于集群的开创性工作，即 boids 模型，并设计了适应度函数的三个组成部分，这些组成部分直接基于他的三个局部规则来强制实现局部分离、聚集和对齐。结果表明，将 Reynolds 规则嵌入适应度函数中可以导致集群行为的成功进化。然而，当适应度得分基于个体对 Reynolds 规则的一致性时，只能进化出局部的、碎片化的集群行为。因此，我们修改了适应度函数的组成部分，以便同时考虑整个代理群体，发现由此产生的行为导致了全局的集群。此外，结果表明，成功进化出集群行为并不一定需要显式地奖励对齐。因此，我们的研究代表了朝着使用进化技术来合成集体行为以完成需要作为一个整体、有凝聚力的群体进行移动的任务迈出了重要一步。
