Automating Large-Scale Detection and Classification of Larger Than Life Cellular Automata Patterns

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Abstract

Cellular automata, particularly Conway’s Game of Life (Life) and its variants, are dynamical systems for which both time and space are discrete. In Life, the most intriguing patterns are known as spaceships and can carry information across long spatial distances. In the early 1990’s, David Griffeath proposed Larger than Life (LtL), which generalizes Life to large neighborhoods and yields a four-dimensional parameter space of two-dimensional cellular automata that exhibit varied and intriguing dynamics. LtL has been extensively studied by Evans, with a focus on the study of “bugs”, which are generalizations of Life’s spaceships. This work leverages scripting to automate various aspects of the study of LtL. In particular, Ismalej has written a suite of Lua scripts that create, simulate, and classify initial configurations for LtL. Building on Evans’ methods that use geometric initial configurations to discover patterns, this approach targets the systematic exploration of LtL parameter space and discovery and analysis of viable patterns such as bugs. Unlike many existing automated search methods that start from random soups and seek any pattern, our approach concentrates on specific geometric configurations, leveraging the resemblance of such configurations to the geometry of LtL’s viable patterns.

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Recommended citation: B. Ismalej and K. M. Evans. (2025). "Automating Large-Scale Detection and Classification of Larger Than Life Cellular Automata Patterns." IEEE Computing and Communication Workshop and Conference (CCWC).