It's a genetic algorithm, which means that it has a fitness function for how close the goal is to being met (choosing the furthest traveled car), and a 'gene',

It's a genetic algorithm, which means that it has a fitness function for how close the goal is to being met (choosing the furthest traveled car), and a 'gene', which is a sequence of instructions. The game utilizes a Neural Network to classify when it should turn/which way it should turn/how it should steer/etc, and then this NN is adapted into the genetic algorithm, which effectively generates random genes, follows each of those genes, then chooses the best 'fit' one according to the fitness function. Next, the algorithm restarts with the exception of now it's randomness is slightly based on the best fit gene from the last epoch, and repeat until you've got a gene that works. Imagine having a wall, and a certain portion of it is sticky (since it's covered in invisible glue), but you don't know which portion, and you certainly don't want to touch it to find out. So you throw spaghetti at the wall, and see which portions stick. Next, you throw more spaghetti at the part that seems to stick, and repeat until you've got the entire sticky portion mapped out. There's an old genetic algorithms demo with smart rockets and path-finding here: http://www.blprnt.com/smartrockets/ http://www.blprnt.com/smartrockets/ [warning: requires flash]. If you're actually interested in building one of these, Daniel Shiffman wrote The Nature of Code: http://natureofcode.com/ http://natureofcode.com/, which is an introductory level book for this kind of thing. He uses Processing in the book itself for modeling, but he also goes over the theories pretty well. If you want some formal literature, I took a brain and behavioral processing class in college, and the professor still has his slides up here: http://www.life.illinois.edu/mcb/419/sessions/index.php http://www.life.illinois.edu/mcb/419/sessions/index.php, along with links to papers and homework scaffolding.