  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.
