Abstract

Cellular automata (CA) are discrete dynamical systems composed of cells that interact locally through simple mechanisms, yet they can display surprisingly complex behaviors. Due to this local interaction, controlling certain global properties of the system is notoriously challenging. We explore how we can control global behavior of stochastic CA by a machine learning approach. This method offers a novel, exploratory perspective to address well-known issues in CA theory.