#Widelands make new soldiers code#

( View large version) Left: the Andes mountains (from NASA's Earth Observatory). Right: a screenshot from a map generated by WorldEngine. Let’s compare some real landscapes to those of WorldEngine: Left: a set of islands north of Japan. Indeed, the best way to generate a realistic world is to simulate the different physical phenomena which shape our world and make it appear as it is. It is difficult to obtain realistic maps without simulating such phenomena. Why is that? Because they appear at the border of tectonic plates. If you look at mountains ranges on Earth you will see that many of them tend to follow a line. You can use simplex noise (a technique to generate random matrices of values between -1 and 1) to generate an elevation map, where brighter color means higher elevation: Simplex noise. There are many ways to generate a world, some of which are very simple. In the rest of this article we’ll take a look at how WorldEngine works. You can be very ambitious and generate an entire world: this is the goal of WorldEngine.

Procedural content generation can generate all sorts of things.

#Widelands make new soldiers code#

A city generated by algorithms in fewer than 100 lines of code on. You can do very complex things with procedural content generation but it can be also very simple. It is an intriguing mix of ASCII art graphics and incredibly deep simulation. You could meet a character and find out that a certain ancestor of his slayed a monster who was haunting the area you’re passing by. In this game an entire world is simulated with its history.

There is one example which is less well known but highly respected by passionate geeks: Dwarf Fortress. Today you could walk in Minecraft until the end of time and keep seeing new land being generated for you. You could play Diablo over and over and find new artifacts and different levels. While there are many different situations in which it’s possible to use procedural content generation, it’s more evident in well-known video games. From a few samples and an algorithm introducing realistic differences you could generate a much larger sample which seems realistic to the eyes of the viewer. Nowadays procedural content generation is also used in movies when there is the need to generate many similar objects: think about a warehouse with thousands of nearly identical items, or an army of soldiers running down a hill. This would be of benefit to risk assessment in flood insurance or government risk-management analyses. Another example would be to simulate flooding by using government-provided digital elevation maps (DEMs) and adjusting various factors like precipitation, ground saturation and density. If you’re able to encode the rules of a phenomenon, you can tweak some parameters and run a simulation to understand what factors are the most important in a scenario. Procedural content generation can also be used to simulate scenarios, such as testing a robot’s control algorithm. If you think about it, you can just save a seed and simply regenerate the world from it each time you play. Fun fact: one of the initial reasons to use procedural content generation was to save memory (think about Daggerfall with its 161,600 square kilometers open terrain to explore).

You would need some intelligence in the algorithm so that all rooms are reachable, or that monsters are properly distributed across the dungeon. Instead of manually writing different levels, defining where walls are or where monsters are hidden, you could use an algorithm to generate that for you. Procedural content generation has been in use for the last 40 years in video games, and it’s a key characteristic of rogue-like games. Making Embedded Content Work In A Responsive iFrame.Conversational Interfaces: Where Are We Today? Where Are We Heading?.Playful UX Design: Building A Better Game.Algorithm-Driven Design: How Artificial Intelligence Is Changing Design.You could generate the landscape of a city, the shape of a tree or an entire world. Typically, you can give a seed or some initial parameters to a procedural content generation algorithm, and get some result. I had the same feeling when I started to play with procedural content generation, which is to find the rules behind a phenomenon, encode them in an algorithm, and use that algorithm to create something virtual, but realistic - a plausible simulation. When I was young and learning to program, I was fascinated by the possibility of creating things that could live inside my monitor.