Update

procgen.md

@@ -10,7 +10,9 @@ For its extreme save of space procedural generation is extremely popular in [dem
 
 We may see procgen as being similar to [compression](compression.md) algorithms: we have large data and are looking for an algorithm that's much smaller while being able to reproduce the data (but here we normally go the other way around, we start with the algorithm and see what data it produces rather than searching for an algorithm that produces given data). [John Carmack](john_carmack.md) himself called procgen "basically a shitty compression".
 
-Using [fractals](fractal.md) is a popular technique in procgen because they basically perfectly fit the definition of it: a fractal is defined by a simple equation or a set of a few rules that yield an infinitely complex shape. Nature is also full of fractals such as clouds, mountain or trees, so fractals look organic.
+Using **[fractals](fractal.md)** (e.g. those in a form of [L-system](l_system.md)) is a popular technique in procgen because fractals basically perfectly fit the definition perfectly: a fractal is defined by a simple equation or a set of a few rules that yield an infinitely complex shape. Nature is also full of fractals such as clouds, mountain or trees, so fractals look organic.
+
+There are also other techniques such as [wave function](wave_function.md) collapse which is used especially in tile map generation. Here we basically have some constraints set (such as which tiles can be neighbors) and then consider the initial map a [superposition](superposition.md) of all possible maps that satisfy these constraints -- we then set a random tile (chosen from those with lowest [entropy](entropy.md), i.e. fewest possible options) to a random specific value and propagate the consequences of it to other tiles causing a cascading effect of collapsing the whole map into one of the possible solutions.
 
 A good example to think of is generating procedural [textures](texture.md). This is generally done by first generating a basis image or multiple images, e.g. with [noise](noise.md) functions such as [Perlin noise](perlin_noise.md) (it gives us a grayscale image that looks a bit like clouds). We then further process this base image(s) and combine the results in various ways, for example we may use different transformations, [modulations](modulation.md), blending, adding color using [color ramps](color_ramp.md) etc. The whole texture is therefore described by a [graph](graph.md) in which nodes represent the operations we apply; this can literally be done visually in software like [Blender](blender.md) (see its [shader](shader.md) editor). The nice thing is that we can now for example generalize the texture to 3 dimensions, i.e. not only have a flat image, but have a whole volume of a texture that can extremely easily be mapped to 3D objects simply by intersecting it with their surfaces which will yield a completely smooth texturing without any seams; this is quite often used along with [raytracing](raytracing.md) -- we can texture an object by simply taking the coordinates of the ray hit as the 3D texture coordinates, it's that simple. Or we can animate a 2D texture by doing a moving cross section of 3D texture. We can also write the algorithm so that the generated texture has no seams if repeated side-by-side (by using modular "wrap-around" coordinates). We can also generate the texture at any arbitrary resolution as we have a continuous mathematical description of it; we may perform an infinite zoom into it if we want. As if that's not enough, we can also generate almost infinitely many slightly different versions of this texture by simply changing the [seed](seed.md) of [pseudorandom](pseudorandom.md) generator we use.