Emergence of Specialised Collective Behaviors in Evolving Heterogeneous Swarms

Natural groups of animals, such as swarms of social insects, exhibit astonishing degrees of task specialization, useful for solving complex tasks and for survival. 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 a different neural network controller associated to each component. The whole genotype, which expresses the behavior of the whole group through the two components, is subject to evolution with a single fitness function. We analyze the obtained behaviors and use the insights provided by these results to design an online regulatory mechanism. Our experiments show four main findings: 1) Heterogeneity improves both robustness and scalability; 2) The sub-groups evolve distinct emergent behaviors. 3) 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. 4) The online regulatory mechanism improves overall performance and scalability.