Learning of intelligent swarm behavior

Principal Investigators:

Henning Sprekeler
Pawel Romanczuk
David Bierbach (Co-PI)
mail: bierbacd@hu-berlin.de

Team members:

Luis Alberto Gómez Nava (Postdoctoral researcher)
Robert Tjarko Lange (doctoral researcher)

When is it ecologically beneficial to act as a collective, or to develop diversity within a swarm? When is it best to act alone?

Research Unit 1, SCIoI Project 12

A central challenge in both understanding and developing swarm intelligence is the relation between the behavior of a swarm of agents and its ecological niche. The goal of this project is to combine analytical behavioral characterizations of biological swarms (specifically, fish) in different ecological settings with synthetic state-of-the-art machine learning methods that optimize the behavior of a multi-agent system for a given task (e.g., (deep) multi-agent reinforcement learning).

The ideal outcome on the analytical side would be a normative, ecological interpretation of observed swarm behavior as an adaptation to an ecological niche, rather than classical phenomenological models that derive collective behavior from the behavior of the individuals.

For example, the suggested optimization approach to swarm behavior can provide insights regarding when it is ecologically beneficial to act as a collective, when to develop diversity within a swarm, and when to act alone.

The ideal synthetic outcome would be the development of novel learning algorithms for artificial swarms of, e.g., robotic agents, that allow a behavioral optimization for a given ecological niche, i.e., an ensemble of tasks. The project aims at establishing essential methods for the implementation of the “collective shepherding” behavior. The developed methodology will serve as a starting point for incorporating priors for specific ecological niches, as well as the sensory and motor aspects of the agents in the swarm.



Related Publications

Zhao, Y., Huepe, C., & Romanczuk, P. (2022). Contagion dynamics in self-organized systems of self-propelled particles. Scientific Reports. https://doi.org/10.1038/s41598-022-06083-0
Winklmayr, C., Kao, A. B., Bak-Coleman, J. B., & Romanczuk, P. (2020). The wisdom of stalemates: consensus and clustering as filtering mechanisms for improving collective accuracy. Proceedings of the Royal Society B: Biological Sciences, 287(1938), 20201802. https://doi.org/10.1098/rspb.2020.1802
Winklmayr, C., Kao, A. B., Bak-Coleman, J. B., & Romanczuk, P. (2023). Collective decision strategies in the presence of spatio-temporal correlations. Collective Intelligence. https://doi.org/10.1177/26339137221148675
Vischer, M., Lange, R., & Sprekeler, H. (2022). On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning. ICLR 2022. https://doi.org/10.48550/arXiv.2105.01648
Rahmani, P., Peruani, F., & Romanczuk, P. (2020). Flocking in complex environments—Attention trade-offs in collective information processing. PLOS Computational Biology, 16(4), e1007697. https://doi.org/10.1371/journal.pcbi.1007697
Poel, W., Daniels, B. C., Sosna, M. M. G., Twomey, C. R., Blanc, S. L., Couzin, I., & Romanczuk, P. (2022). Subcritical escape waves in schooling fish. Science Advances. https://doi.org/10.1126/sciadv.abm6385
Poel, W., Winklmayr, C., & Romanczuk, P. (2021). Spatial Structure and Information Transfer in Visual Networks. Frontiers in Physics. https://doi.org/10.3389/fphy.2021.716576
Lukas, J., Romanczuk, P., Klenz, H., Klamser, P., Arias Rodriguez, L., Krause, J., & Bierbach, D. (2021). Acoustic and visual stimuli combined promote stronger responses to aerial predation in fish. Behavioral Ecology, arab043. https://doi.org/10.1093/beheco/arab043
Lukas, J., Auer, F., Goldhammer, T., Krause, J., Romanczuk, P., Klamser, P., Arias-Rodriguez, L., & Bierbach, D. (2021). Diurnal Changes in Hypoxia Shape Predator-Prey Interaction in a Bird-Fish System. Frontiers in Ecology and Evolution, 9. https://doi.org/10.3389/fevo.2021.619193
Lange, R., & Sprekeler, H. (2022). Learning not to learn: Nature versus Nurture in Silico. AAAI 2022. https://doi.org/10.48550/arXiv.2010.04466
Klamser, P. P., Gómez-Nava, L., Landgraf, T., Jolles, J. W., Bierbach, D., & Romanczuk, P. (2021). Impact of Variable Speed on Collective Movement of Animal Groups. arXiv:2106.00959 [physics, q-bio]. https://doi.org/10.3389/fphy.2021.715996
Klamser, P., & Romanczuk, P. (2021). Collective predator evasion: Putting the criticality hypothesis to the test. PLOS Computational Biology. https://doi.org/10.1371/journal.pcbi.1008832
Gómez-Nava, L., Bon, R., & Peruani, F. (2022). Intermittent collective motion in sheep results from alternating the role of leader and follower. Nature Physics, 8. https://doi.org/10.1038/s41567-022-01769-8
Gómez-Nava, L., Lange, R. T., Klamser, P. P., Lukas, J., Arias-Rodriguez, L., Bierbach, D., Krause, J., Sprekeler, H., & Romanczuk, P. (2023). Fish shoals resemble a stochastic excitable system driven by environmental perturbations. Nature Physics. https://doi.org/10.1038/s41567-022-01916-1
Doran, C., Bierbach, D., Lukas, J., Klamser, P., Landgraf, T., Klenz, H., Habedank, M., Arias-Rodriguez, L., Krause, S., Romanczuk, P., & Krause, J. (2022). Fish waves as emergent collective antipredator behavior. Current Biology. https://doi.org/10.1016/j.cub.2021.11.068
Davidescu, M. R., Romanczuk, P., Gregor, T., & Couzin, I. D. (2023). Growth produces coordination trade-offs in Trichoplax adhaerens, an animal lacking a central nervous system. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.2206163120
Daniels, B. C., & Romanczuk, P. (2021). Quantifying the impact of network structure on speed and accuracy in collective decision-making. Theory in Biosciences. https://doi.org/10.1007/s12064-020-00335-1