Procedural Generation on Programmer.ie: Modern AI programming

Cellular Automata From First Principles 16: Generate Textures with Local Rules

Mon, 10 Aug 2026 18:50:00 +0100

Cellular Automata From First Principles 16: Generate Textures with Local Rules

Cellular automata do not need to represent a literal physical system.

They can also be used as visual machines.

The same ingredients we have used throughout the book:

local state
neighborhood perception
shared update
repetition

can generate masks, growth patterns, surface variation and animation.

The evaluation question changes.

Instead of asking:

Is this physically accurate?

we ask:

Does this local process produce useful, controllable visual structure?

Cellular Automata From First Principles 15: Grow Terrain from Local Height Rules

Mon, 10 Aug 2026 18:48:00 +0100

Cellular Automata From First Principles 15: Grow Terrain from Local Height Rules

Binary caves ask:

wall or floor?

Terrain needs richer state.

Let each cell store a height:

0.0 = low
1.0 = high

Now local rules can smooth, raise, erode and classify terrain.

The challenge is not creating a pretty array.

It is creating a process whose output we can explain and control.

Start with a height field

import numpy as np


def random_heightmap(
    rows=120,
    cols=160,
    seed=42,
):
    rng = np.random.default_rng(seed)

    return rng.random(
        (rows, cols)
    )

Raw independent noise contains variation but little large-scale geography.

Cellular Automata From First Principles 14: Generate Caves from Noise

Mon, 10 Aug 2026 18:46:00 +0100

Cellular Automata From First Principles 14: Generate Caves from Noise

A cave generator can be built from a mechanism we already understand:

random initial cells
       ↓
count nearby walls
       ↓
apply local smoothing rule
       ↓
repeat a few times
       ↓
stop and use the result

Unlike a forest-fire simulation, we are not trying to model an indefinitely evolving world.

Here the cellular automaton is a construction process.