# behav: swarms and hybrids, an approach to create and control collective motions (on demand)

AA << demonstrate that it is possible to generate coordinated structures in collective behavior at desired moments with intended global patterns by fine-tuning an inter-agent interaction rule. (Their) strategy employs deep neural networks, obeying the laws of dynamics, to find interaction rules that command desired collective structures. The decomposition of interaction rules into distancing and aligning forces, expressed by polynomial series, facilitates the training of neural networks to propose desired interaction models. Presented examples include altering the mean radius and size of clusters in vortical swarms, timing of transitions from random to ordered states, and continuously shifting between typical modes of collective motions. This strategy can even be leveraged to superimpose collective modes, resulting in hitherto unexplored but highly practical hybrid collective patterns, such as protective security formations. >>

Dongjo Kim, Jeongsu Lee, Ho-Young Kim. Navigating the swarm: Deep neural networks command emergent behaviours. arXiv: 2407.11330v1 [cs.NE]. Jul 16, 2024.️

https://arxiv.org/abs/2407.11330

Also: swarm, flock, behav, AI (artificial intell), in https://www.inkgmr.net/kwrds.html 

Keywords: behav, swarm, flock, AI, artificial intelligence