  Cellular automata (CA) captivate researchers due to teh emergent, complex
individualized behavior that simple global rules of interaction enact. Recent
advances in the field have combined CA with convolutional neural networks to
achieve self-regenerating images. This new branch of CA is called neural
cellular automata [1]. The goal of this project is to use the idea of idea of
neural cellular automata to grow prediction machines. We place many different
convolutional neural networks in a grid. Each conv net cell outputs a
prediction of what the next state will be, and minimizes predictive error.
Cells received their neighbors' colors and fitnesses as input. Each cell's
fitness score described how accurate its predictions were. Cells could also
move to explore their environment and some stochasticity was applied to
movement.
