OpenNefia VS WaveFunctionCollapse

The code for all these mods lives here, if you're curious to see what the modding system looks like right now. I think that, even if some of the mods are incomplete in their current state, they implement several of the useful features that I was wanting to port from the start, so having any amount of progress towards completing them isn't a bad thing. They're also implemented as separate mods, so if they get too buggy they can be disabled easily.

Well, it's not Elisp, but:

https://github.com/Ruin0x11/OpenNefia

It's an engine rewrite of an old roguelike I used to play in Lua. I'm trying to experiment with making a game where the engine is similar in flexibility to Emacs.

It has an Emacs frontend, and I designed it with the zealotry of an Emacs user, meaning it has advice, hooks, interactive evaluation and runtime module hotloading. You can run anything the engine can run from a REPL (and cause all the state to become broken easily).

I recently implemented nefias in my engine rewrite of Elona, called OpenNefia. Hopefully the ability to generate lots of new dungeons in addition to the ones in vanilla would make the game more interesting. Here are few I made, as part of a mod:

An engine rewrite of a Japanese roguelike I played a lot. I liked Emacs, so I decided to see what would happen if I tried writing it in the style of what Steve Yegge calls "living systems", where all the code is interactively callable in-game and reusable in mods. There is no scripting layer, the implementation and extension language are one and the same (Lua). I like to think of the engine as a massive programming runtime with a bunch of libraries and functions made for the sole purpose of modding the game. You could whip up a scratch buffer and start tinkering around with the game state or prototyping new mods fairly quickly.

The engine is not general purpose either, it's specific to the quirks of the original game. The number of weird ideas that I could graft onto it keeps increasing with each week. Yet, without feature parity and stability with the original, it's a long way away from having those things.

Another downside is going back and playing the original now isn't as fun, because I keep thinking I'm playing the rewrite and expecting bugs to pop up at ever corner. Working on a project like this for so long affects your perception of the end result in ways you can't easily unsee.

Also gets pretty lonely working on something alone for years you're not sure anyone will care about when it's playable.

[1] https://github.com/Ruin0x11/OpenNefia

Thank you! And yes, I agree. I was looking at uh https://github.com/mxgmn/WaveFunctionCollapse and wondering if that were applicable here :)

Have a good day!

Intuitively, it measures the difference between two probability distributions. It's not symmetric, so it's not quite that, but in my opinion, it's good intuition.

As motivation, say you're an internet provider, providing internet service to a business. You naturally want to save money, so you perhaps want to compress packets before they go over the wire. Let's say the business you're providing service to also compresses their data, but they've made a mistake and do it inefficiently.

Let's say the business has, incorrectly, determined the probability distribution for their data to be $q(x)$. That is, they assign probability of seeing symbol $x$ to be $q(x)$. Let's say you've determined the "true" distribution to be $p(x)$. The entropy, or number of bits, they expect to transmit per packet/symbol will be $-\sum p(x) lg(q(x))$. Meaning, they'll compress their stream under the assumption that the distribution is $q(x)$ but the actually probability of seeing a packet, $x$, is $p(x)$, which is why the term $p(x) lg(q(x))$ shows up.

The number of bits you're transmitting is just $-\sum p(x) lg(p(x))$. Now we ask, how many bits, per packet, is the savings of your method over the businesses? This is $-\sum p(x) lg(q(x)/p(x))$, which is exactly the Kullback-Leibler divergence (maybe up to a sign difference).

In other words, given a "guess" at a distribution and the "true" distribution, how bad is it between them? This is the Kullback-Leibler distribution and why it shows up (I believe) in machine learning and fitness functions.

As a more concrete example, I just ran across a paper talking [0] about using WFC [1] to asses how well it, and other algorithms, do when trying to create generative "super mario brothers" like levels. Take a 2x2 or 3x3 grid, make a library of tiles, use that to generate a random level, then use the K-L divergence to determine how well your generative algorithm has done compared to the observed distribution from an example image.

[0] https://arxiv.org/pdf/1905.05077.pdf

[1] https://github.com/mxgmn/WaveFunctionCollapse

Reminds me of wave function collapse - a programmatic way to generate mazes.

I am using WFC to generate the terrain, with pretty much a copy-paste implementation of the original WFC implemented into Unity.

If you still want to go the WFC route, the original WFC repository is a great place to start. There's also a (relatively barebones looking) Godot plugin you could take a look at.

wave function collapse studies - this is done with the https://github.com/mxgmn/WaveFunctionCollapse algorithm after I saw https://github.com/CodingTrain/Wave-Function-Collapse mention it. done in P5! IG https://www.instagram.com/ronivonu/

There’s an interesting approach similar to this called [Wave Function Collapse](https://github.com/mxgmn/WaveFunctionCollapse) (no relation to wfc in physics idea besides inspiration). It can infer the probabilistic constraints from one input example, and it seems to generalize quite well. Here’s a [little demo](https://oskarstalberg.com/game/wave/wave.html)