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Sample data: Performance prediction of hub-based swarms

Jain, Puneet1; Dwivedi, Chaitanya2; Smith, Nick1; Goodrich, Michael1

Published Nov 12, 2024 on Dryad. https://doi.org/10.5061/dryad.hhmgqnkrp

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Nov 12, 2024 version files 53.34 MB

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Abstract

There are powerful tools for modeling 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 that have similar performance have very similar embeddings. Such embeddings are used to classify swarm states 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.