## Exploring cellular automata 1

Conway’s game of life is maybe the most well known ruleset for cellular automata. Each cell in a grid has some possible set of states, and set of rules for changing state based on its own and it’s (usually) immediate neighbor cells. Time elapses with a number of discrete steps, sometimes called epochs. In Game of Life, a cell can either be “alive” or “dead” (on or off), and will live for the next time step only if it has one or two alive neighbors.

Some incredible patterns emerge, including spaceships, guns (creating gliders and spaceships), breeders (creating guns), other puffersrakes, long-lived methuselahs (this example creating some gliders).

Other rulesets lead to neat patterns. Here’s a fun one with many cell states. It lets you rate the results as it scans some starting parameter space.

So, we’re certainly not limited to one ruleset. Coming up with one is easy — finding if there are any neat patterns maybe not so much. It might be better to first explore simpler rules — ones in only 1 dimension. In this case, a linear space is segmented into cells, and each cell has only two neighbors. Given some configuration of three cells in a row, the middle cell’s state in the next epoch will be dependent on its own state as well as the two alongside. When considering how to change any cell, since there are only three cells to consider, and only two possible states for each, there are only $2^3 = 8$ arrangements to consider when changing any cell. Because of this small number, it’s no problem to display the rule of the automaton as a list of diagrams, each showing one of the eight starting arrangements and the resulting state of the middle cell in the next epoch.

With some initial state, for example:

The next line is produced by scanning over this line and matching the top row with the one from the rule icons.

Note that this wraps around, connecting the two edges. This process then continues indefinitely for successive lines. We’re free to pick any length of line and any arrangement of on and off cells as an initial condition, but once this is done, the rest is automated. This particular initial condition for this rule ends up looking like this:

This is kind of neat, but these processes don’t get to show off unless they’ve got some room to work with. Here’s the same rule applied to an initial state of a single black cell in a long row of white.

The repeating triangles are a pattern characteristic of some of the more interesting rules. Rules 30, 90, 110, and 184 are supposedly the most cited in scientific or mathematics articles.

Because there are only 8 arrangements of cells we need to specify to determine a 1-D ruleset, and each arrangement offers the choice of the middle cell turning on or off, there are $2^8 = 256$ possible rules of this type. You can see links to them all here. One could argue there are only 128 rules, 64, or even 32, since negating all the colors, taking a mirror image, or negating the mirror image doesn’t change the behavior much. This means each rule is one of a set of four that operate in the same way.

I’ve already linked to the Wolfram Atlas a few times. The images above were taken from there or made using Mathematica. Stephen Wolfram, of Mathematica fame, is also well-known for his work on cellular automata. He’s argued that these things may be more fundamental to nature than mathematics. It’s considered controversial. I haven’t read A New Kind of Science, a book where he lays this all out, but it’s free and online. Maybe worth checking out. It seems like some of the argument hinges on the fact that some of these automata are Turing complete — they can be set up to operate and produce results like any computer program.

I’d just like to note that my interest in cellular automata is based pretty much entirely on having fun. It’s 11 o’ clock on a Saturday night.

A step after checking out the 1-D rules is to explore the 2-D rules. Next I’ll show off a rule and try to do something interesting with it.