Abstract
We consider the dynamics of artificial chemistry systems consisting of small, interacting neural-network particles. Although recent explorations into properties of such systems have shown interesting phenomena, like self-replication tendencies, social interplay, and the ability for multi-objective applications, most of these settings are reasoned about in the abstract weight space. We extend this setup to involve an applied, stateful positioning task with mutual dependencies and show that stable configurations can be found jointly in both the weight space and 3D space. We show that the main contributing factor is enabling the networks to self-adapt their interaction rates depending on their internal stability or their ability to position themselves correctly. We find that this method effectively prepares the network assembly against potentially destabilizing interactions, promoting emergent stability while preventing convergence to trivial states.