Dynamic Clustering

Consider a history of the enemy state (log, array, whatever). For each entry you have multiple data on the enemy, like distance, speed, distance to wall, etc. Now, DynamicClustering consists in categorizing those entries into clusters, so that each cluster contains "similar" entries. "Similar" is defined by beeing close to each other, the measure of distance beeing the euclidian distance between entries: sqrt(sqr(dist1 - dist2) + sqr(speed1 - speed2) + ...). If you search Google for Clustering algorithms, you'll get lots of references to K-means clustering, which involves iteratively changing the centers of the clusters and reassigning each entry to it's new cluster. I tried it, it's very slow and I didn't manage to make it work good enough. My aproach is simpler, you just take the current entry and consider it the center of the cluster, then you find the N closest entries in the log and use them to decide where to shoot.

Now for the wheighting of each dimension. Each dimension can be wheighted by some factor. Those factors can be decided intuitively (is velocity more important than distance to wall?), by trial and error, or you can use some statistical entities to dynamically decide for you. I tried many ;), the latest beeing the entropy association between each dimension and the hitting GuessFactor (yes, I currently use GuessFactors in Shadow's gun ;)). Read more about it here.

For a slightly simplified version of my DC gun used in Shadow and Tron see: DCBot