Natural groups of animals, such as swarms of social insects, exhibit astonishing degrees of task specialization, useful to address complex tasks and to survive. This is supported by phenotypic plasticity: individuals sharing the same genotype that is expressed differently for different classes of individuals, each specializing in one task. In this work, we evolve a swarm of simulated robots with phenotypic plasticity to study the emergence of specialized collective behavior during an emergent perception task. Phenotypic plasticity is realized in the form of heterogeneity of behavior by dividing the genotype into two components, with one different neural network controller associated to each component. The whole genotype, expressing the behavior of the whole group through the two components, is subject to evolution with a single fitness function. We analyse the obtained behaviors and use the insights provided by these results to design an online regulatory mechanism. Our experiments show three main findings: 1) The sub-groups evolve distinct emergent behaviors. 2) The effectiveness of the whole swarm depends on the interaction between the two sub-groups, leading to a more robust performance than with singular sub-group behavior. 3) The online regulatory mechanism enhances overall performance and scalability.

翻译：自然动物群体（如社会性昆虫的群集）展现出惊人的任务专门化程度，这对解决复杂任务和生存至关重要。这种专门化基于表型可塑性：基因型相同的个体因不同类别个体表达差异，各自专攻特定任务。本研究通过进化具有表型可塑性的模拟机器人群体，探究在涌现感知任务中专门化集体行为的产生机制。表型可塑性通过行为异质性实现：将基因型分为两个组件，每个组件关联不同的神经网络控制器。整个基因型通过两个组件共同表达群体行为，并受单一适应度函数的进化约束。我们分析了所得行为，并据此设计在线调控机制。实验揭示三项核心发现：1）子群进化出不同的涌现行为；2）全群体效能依赖于两个子群间的交互作用，相比单一子群行为展现出更强的鲁棒性；3）在线调控机制可提升整体性能与可扩展性。