  Emergent processes in complex systems such as cellular automata can perform
computations of increasing complexity, and could possibly lead to artificial
evolution. Such a feat would require scaling up current simulation sizes to
allow for enough computational capacity. Understanding complex computations
happening in cellular automata and other systems capable of emergence poses
many challenges, especially in large-scale systems. We propose methods for
coarse-graining cellular automata based on frequency analysis of cell states,
clustering and autoencoders. These innovative techniques facilitate the
discovery of large-scale structure formation and complexity analysis in those
systems. They emphasize interesting behaviors in elementary cellular automata
while filtering out background patterns. Moreover, our methods reduce large 2D
automata to smaller sizes and enable identifying systems that behave
interestingly at multiple scales.
