Visualizing computation in large-scale cellular automata

TL;DR

This paper introduces novel methods for analyzing large-scale cellular automata by employing coarse-graining techniques like frequency analysis, clustering, and autoencoders to uncover emergent structures and behaviors across scales.

Contribution

It presents innovative techniques for coarse-graining cellular automata, enabling the study of large-scale structures and behaviors that were previously difficult to analyze.

Findings

Facilitates discovery of large-scale structure formation.

Filters out background patterns to highlight interesting behaviors.

Reduces 2D automata size for multi-scale analysis.

Abstract

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.