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Animal and robotic collective behaviours can exhibit complex dynamics thatrequire multi-level descriptions. Here, we are interested in developing amulti-level modeling framework for the use of robots in studies about animalcollective decision-making. In this context, using robots can be useful forvalidating models in silico, inducing calibrated repetitive stimuli to triggeranimal responses or modulating and controlling animal collective behaviour.However, designing appropriate biomimetic robotic behaviour faces a majorchallenge: how to go from the collective decision dynamics observed withanimals to an actual algorithmic implementation in robots. In previous work,this was mainly done by hand, often by taking inspiration from human-designedmodels. Typically, models of behaviour are either macroscopic, differentialequations of the population dynamics, or microscopic,explicit spatio-temporalstate of each individual. Only microscopic models can easily be implemented asrobot controllers. Here, we address the problem of automating the design oflower level description models that can be implemented in robots and exhibitthe same collective dynamics as a given higher level model. We applyevolutionary algorithms to simultaneously optimise the parameters of modelsaccounting for different levels of description. This methodology is applied toan experimentally validated shelter-selection problem solved by gregariousinsects and robots. We successfully design and calibrate automatically both amicroscopic and a hybrid model exhibiting the same dynamics as a macroscopicone. Our framework can be used for multi-level modeling of collective behaviourin animal or robot populations and bio-hybrid systems.

Automated optimisation of multi-level models of collective behaviour in a mixed society of animals and robots