Procedural generation is everywhere—you’ve probably encountered it without even realising. It’s what gives in-game worlds their rolling hills, jagged cliffs, and winding cave systems. And at the heart of it all is Perlin noise: a special kind of randomness that isn’t entirely random at all. It’s smooth where it needs to be, rough when we want it to be, and endlessly customizable.
Alan Turing, a pioneering figure in the world of Computer Science, is often celebrated for his work on breaking the Enigma code during World War II. However, one of his lesser-known achievements is creating one of the first chess algorithms — a small yet significant early step in the development of artificial intelligence systems.
This is exciting! We’re nearly ready to start simulating a whole game of Chess. There’s just a few more components we need.
To begin, let’s first consider what information we need to encode to fully describe a move.
In standard chess move notation, we are given only the type of piece, and it’s new location. E.g. Ne2 which indicates that a knight should be moved to the square e2.
If you haven’t already heard, Strava provide a powerful API that allows users to access their data using code. This gives us the ability to create interesting visualisations, analyse progress, and create custom trackers and planners, all with just a few lines of code.
This is part 2 in a series of articles documenting my process of building a chess engine. I hope to share insights from my previous experience, and explain some of the concepts and techniques I’ll be using. If you haven’t already read part 1, I’d strongly recommend taking a look before reading part 2. You can check it out at the link above.
Time to pick up where we left of…
Strava offers a powerful Web API that allows users to access and analyse data from their activities. Whether you’re a fitness enthusiast looking to track your progress or a developer interested in building a simple app that leverages activity data, the Strava API provides a wealth of possibilities.
Looking at the images above, we see an example of an image posterization filter that gives images a cartoon-like appearance, but behind the scenes, this filter is actually using a machine learning algorithm known as clustering.
Before exploring into how this process works and seeing how we can implement it in Python, let’s take a look at why we might want to do this in the first place.
This is not my first rodeo.
I’ve built a chess engine before, but the results were… let’s say… underwhelming.
Don’t get me wrong, the program worked; it was able to play a full game of chess, and for that, I give it credit. But let’s face it, it was terrible at chess.
The Travelling Salesman Problem, TSP, describes a scenario where a salesman wishes to visit a number of cities, while taking the shortest possible route, before returning home to the start point. While it may appear simple, this problem not only has no known polynomial time solution, but there is also no time-efficient way to prove that a given solution is in fact optimal.
