Project

RuleBook

| Boris

I’m releasing a new open source library, RuleBook. Inspired by Inform 7‘s rulebook feature, it’s a way to make functions by defining a set of rules to evaluate. RuleBooks let you define functions are easy to mod and update at runtime.

Full Docs

What are rulebooks?

Rulebooks are essentially a fancy form of C#’s Func<> and Action<> generics.

Func<> and Action<> can hold a reference to a C# method, or an lambda expression/statement. But the thing they hold is essentially a black box – you cannot do much with it except run it, or check equality.

RuleBook provies FuncBook<> and ActionBook<>, which work similarly to their counterparts. But these rulebook objects are built out of individual rules, which can be individually inspected and mutated.

Overall, rulebooks give a systematic way of handling a bunch of useful programming patterns, including events, multiple dispatch, modding and code weaving.

Rulebooks are not an elaborate rules evaluation engine, it’s a lightweight way of stitching together bits of functionality.

Generative Generators

| Boris

This is a experiment I tried out for ProcJam 2023. I wasn’t getting great results from it and got bored after a few days, but I’ve decided to share what I did manage.

The rules of the hackathon were changed this year, ruling out most forms of AI. I was thinking – what’s the furthest I can push that rule without crossing the line?

In the end, I designed a system where we used AI to design and build a standalone classical generator.

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nice-hooks

| Boris

I’ve created a new open source library.

I’ve been learning quite a bit about AI and AI Alignment recently. A few weeks ago I joined the Interpretability Hackathon. Sadly my contributions were minimal as I had to leave halfway through, but doing it made me appreciate how bad the tooling is in this area.

So I’ve created nice-hooks, a library for working with pytorch hooks and activations more effectively.

GitHub

Docs

Announcing Sylves

| Boris

I’ve released a new library, Sylves that handles the geometry of grids for C# or Unity. I’ve basically distilled all my knowledge from several different grid projects, and made a solid base for anything you might want.

Constrained Text Generation with AI

| Boris

I was discussing how AI text generation, such as ChatGPT, might end up getting used in computer games. So far, designers are fairly reluctant to adopt the technology. One of the key problems is that you just can’t control the output enough. Language models will break character or respond in inappropriate and toxic ways. Finding a good solution to this is a huge research field, and not likely to get cracked soon.

For the foreseeable future, AI in games is much more likely to be used offline – assets and dialog generation generated up front, so it can be vetted before being integrated into the game.

But it got me thinking, can we vet the AI’s output in advance, but still get the benefits of intelligent decision making at runtime? It turns out, we can! I doubt it’ll be useful in every circumstance, but I can certainly see uses for it, like chatbots, games.

The code and demonstration for this article is available here.

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VoronatorSharp

| Boris

I’ve relased a new library, VoronatorSharp.

VoronatorSharp is a C# library that computes Voronoi diagrams. The Voronoi diagram for a collection of points is the polygons that enclose the areas nearest each of those sites.

Voronoi diagrams have applications in a number of areas such as computer graphics.

This library features:

  • Computes Voronoi diagrams and Delaunay triangulations.
  • Voronoi polygons can be clipped to a rectangular area.
  • Uses a n log(n) sweephull algorithm.
  • The implementation attempts to minimize memory allocations.
  • Integrates with Unity or can be be used standalone.
  • Uses robust orientation code.
  • Handles Voronoi diagrams with only 1 or 2 points, and collinear points.