Dynamic Clustering

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Dynamic clustering is a technique to find entries in your log similar to the current situation. Essentialy, it is a K-nearest neighbor algorithm, and not actually clustering at all. Despite this midnomer, the term "Dynamic Clustering" has stuck with the robocode community.

The ideas is to record a "state" (or termed "situation") for each entry in your log. The state can contain any data that you seem valuable, such as lateral velocity, advancing velocity, or enemy distance. Save this along with your data. Then to use the day, you find a "distance" between current state and past states. Distance can be Euclidian (sqrt(sqr(dist1 - dist2) + sqr(lat1 - lat2))) or another way, such as Manhattan distance. Find some number of entries with the lowest distance, and use them for targeting, movement, or whatever you like.

The earliest method doing this, was by iterating through the log and calculating the distance for each log entry. If you have a large log this is very slow. More recently Kd-Trees have been used. Corbos was the first one to mention them in Robowiki, which caught the interest of Chase-san and Simonton. As of now, Simonton's Kd-tree implementations is one of faster ones.

Bot using this technique

See Also