John Conway’s “Game of Life” was one of the first things I ever wrote in Java, back in the days when we were using 1.1. This is a slight variation on the traditional 2D view, where the alife simulation is wrapped around a spinning globe. The results are shown below, along with the link to the web page containing the applet.
Conways Game of Life

Conway's Game of Life

The way this was created was as follows:

Step 1 – Create the Planet Mesh

I’ve defined my axes with x to the right, z up and y into the screen. This is slightly unusual, but maps to the ground plane which was originally XZ.

for z=0 to numZ-1
    for x=0 to numX-1
          coords[x][z]=new Point3D(cx,cy,cz);

The mesh of points can be wrapped around in the x direction, but not Z, so we need an extra line of points at the South pole. I’ve also taken the radius to be 1.0 as using the unit sphere simplifies a lot of the graphics calculations that follow.

This gives you the following result:

Planet Mesh

Planet Mesh

Step 2 – Spin the World

Next I added an animation thread that increments ‘A’, the angle of rotation of the planet. In the rendering code for the mesh I rotate the points to spin the planet around the poles. The interesting thing here is that you don’t need the Y coordinate as there’s no projection, so that saves a few operations.


The back faces have been removed by using the direction between the surface normal and the viewer. Any face pointing away from the viewer is not drawn.

Step 3 – Add the Game of Life Simulation

I already had an implementaion of this in Java, so I just pulled it into the project. The following wikipedia article contains everything you could every need to know about Conway’s Game of Life:’s_Game_of_Life

Then it’s just a case of running the ALife simulation and linking the output to the cells in the planet mesh. The grid used for the Game of Life simulation and the mesh making up the planet are the same size, so there is a simple one to one relationship.

Game of Life Planet

Game of Life Planet


Step 4 – Lights

To improve the realism, I added some lighting using Lambert’s cosine rule. The direction of the light is [-1.0, 0.0, 0.0] which makes the intensity calculation straightforward. The light is assumed to be far enough away that the direction of the light is constant over the whole object. The planet is a unit sphere centred on the origin, so the normal to the surface patch is just a ray through the origin and the centre of the patch. I’ve actually taken the top left corner to save having to calculate the centre point, but it doesn’t make much difference to the effect.

According to Lambert’s cosine rule, the intensity of the patch is proportional to the cosine of the angle between the surface normal and the light. We use the dot product of the two vectors to get the cosine of the acute angle between them. As both vectors are already normalised beforehand, we don’t have to normalise them ourselves.

In this view, any game of life cell that is ‘on’ is drawn in blue, while any that are ‘off’ are white. The white colour uses the diffuse lighting while the blue is drawn as emissive so you can see the patterns as they go around the dark side of the globe. I’ve also added a line at the 0 and 180 degree longitude positions so you can see the planet rotating.

Here is an image of a “Gosper Glider Gun” about to shoot gliders at itself from around the other side of the planet. The applet link below contains a number of the more common patterns.

Next Steps:
The mapping of the life grid to the planet mesh could do with some improvement. Anything moving east or west maps around the sphere correctly, but anything moving through the north pole reappears at the south and vice-versa. There are better ways to map grids onto spheres, but that’s for next time. I also have an erosion-based model that I wrote a long time ago to create realistic looking land and water masses, which this was was originally intended for.

Crowd, transport and urban simulations are at their roots down to ‘Agents’ or ‘Objects’ that are assigned a set of rules as to how to moves in relation to both the environment and other agents around them. 3D Studio Max has a built in ‘Crowd and Delegate’ system which can be used to assign behaviour and therefore create realistic traffic of pedestrian systems in 3D space.

The movie below displays our first tentative steps to explore emergent behaviour via the introduction of simple rules. The movie starts out with a basic ‘wander’ behaviour where the agents only knowledge is the shape of the surface. Moving on we assign each of our ‘cubes’ (of which we have become quite fond of…) a level of vision so they can see ahead and therefore avoid each other and objects in their environment.

Crowd and Delegates – Emergent Behaviour from digitalurban on Vimeo.
Thirdly, the agents seek a ‘sphere’ which could be viewed as a source of food. While being aware of each other and tweaking the way the cubes move a swarm behaviour emerges. Finally, we introduce competing groups with two priorities, firstly to eat and secondly to stay as a group. The majority choose the group over the food but a couple stray off in search of sustenance and lose the other members.

All of these models are going into our exhibition space previewed below to allow a step by step guide to the principles of agent based modelling.

The virtual exhibition space should be online for Windows and Mac in the next few weeks.