Performance prediction of hub-based swarms.

There are powerful tools for modelling swarms that have strong spatial structures like flocks of birds, schools of fish and formations of drones, but relatively little work on developing formalisms for other swarm structures like hub-based colonies doing foraging, maintaining a nest or selecting a new nest site. We present a method for finding low-dimensional representations of swarm state for simulated homogeneous hub-based colonies solving the best-of-N problem. The embeddings are obtained from latent representations of convolution-based graph neural network architectures and have the property that swarm states which have similar performance have very similar embeddings. Such embeddings are used to classify swarm state into binned estimates of success probability and time to completion. We demonstrate how embeddings can be obtained in a sequence of experiments that progressively require less information, which suggests that the methods can be extended to larger swarms in more complicated environments.This article is part of the theme issue 'The road forward with swarm systems'.