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.
You’ve swiped through dozens of profiles, but why does it feel like there’s no perfect match for you? What if there was another way where we could keep everyone happy?
I think it’s time to play Cupid! (But be warned: it’s not all sunshine and rainbows; there’s some tough decisions ahead)
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 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…
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.
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.
Imagine you want to travel around Europe. You want to see the sights, and visit as many European capital cities as possible, but you’re on a budget; flights can get expensive, so you plan to travel by rail and bus. To make your funds stretch as far as possible, you need to plan a route that will take you to all of the cities on your list for the cheapest price, before heading home again.
In the ever-expanding field of artificial intelligence, some of the most fascinating advancements come from mimicking the natural world. One such breakthrough is the Genetic Algorithm, a powerful machine learning technique inspired by Charles Darwin’s theory of natural selection. Just as organisms evolve over time, honing their adaptations to thrive in their environments, Genetic Algorithms evolve solutions to complex problems, becoming increasingly effective with each iteration.
