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Swarm behavior emerges from the local interaction of agents and theirenvironment often encoded as simple rules. Extracting the rules by watching avideo of the overall swarm behavior could help us study and control swarmbehavior in nature, or artificial swarms that have been designed by externalactors. It could also serve as a new source of inspiration for swarm robotics.Yet extracting such rules is challenging as there is often no visible linkbetween the emergent properties of the swarm and their local interactions. Tothis end, we develop a method to automatically extract understandable swarmcontrollers from video demonstrations. The method uses evolutionary algorithmsdriven by a fitness function that compares eight high-level swarm metrics. Themethod is able to extract many controllers (behavior trees) in a simplecollective movement task. We then provide a qualitative analysis of behaviorsthat resulted in different trees, but similar behaviors. This provides thefirst steps toward automatic extraction of swarm controllers based onobservations.

Understandable Controller Extraction from Video Observations of Swarms