Emergent interactions lead to collective frustration in robotic matter

TL;DR

This paper investigates how collective behaviors and frustration emerge in robotic matter systems composed of many neural-network-enabled particles, revealing phase transitions and self-organization phenomena.

Contribution

It introduces a stochastic many-particle model with neural networks to study emergent behaviors, including phase transitions and frustration, in robotic matter systems.

Findings

Robotic matter exhibits complex emergent phenomena such as phase transitions and particle species formation.

A density-dependent phase transition with criticality signatures was identified.

Self-organization mediated by emergent interactions explains the phase transition.

Abstract

Current artificial intelligence systems show near-human-level capabilities when deployed in isolation. Systems of a few collaborating intelligent agents are being engineered to perform tasks collectively. This raises the question of whether robotic matter, where many learning and intelligent agents interact, shows emergence of collective behaviour. And if so, which kind of phenomena would such systems exhibit? Here, we study a paradigmatic model for robotic matter: a stochastic many-particle system in which each particle is endowed with a deep neural network that predicts its transitions based on the particles' environments. For a one-dimensional model, we show that robotic matter exhibits complex emergent phenomena, including transitions between long-lived learning regimes, the emergence of particle species, and frustration. We also find a density-dependent phase transition with signatures of criticality. Using active matter theory, we show that this phase transition is a consequence of self-organisation mediated by emergent inter-particle interactions. Our simple model captures key features of more complex forms of robotic systems.