演化异质群体中专门化集体行为的涌现
Emergence of specialized Collective Behaviors in Evolving Heterogeneous Swarms
AI总结：
本研究通过演化具有表型可塑性的模拟机器人群体，发现子群体涌现出专门化行为，群体性能依赖子群体交互，并设计了在线调节机制以提升整体性能和可扩展性。
摘要：
自然动物群体，例如社会性昆虫的群体，展现出惊人的任务专门化程度，这对于应对复杂任务和生存至关重要。这种专门化由表型可塑性支持：共享相同基因型的个体，其基因型在不同类别的个体中表达不同，每个类别专门负责一项任务。在本工作中，我们演化了一个具有表型可塑性的模拟机器人群体，以研究在涌现感知任务中专门化集体行为的出现。表型可塑性通过将基因型分为两个组成部分来实现行为的异质性，每个组成部分关联一个不同的神经网络控制器。整个基因型通过这两个组成部分表达整个群体的行为，并受单一适应度函数的演化支配。我们分析了所获得的行为，并利用这些结果提供的见解设计了一个在线调节机制。我们的实验展示了三个主要发现：1）子群体演化出不同的涌现行为。2）整个群体的有效性取决于两个子群体之间的交互，从而产生比单一子群体行为更稳健的性能。3）在线调节机制增强了整体性能和可扩展性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
