Hard-coded segmentation

I know what you mean about those Hard-coded segmented bin-based statistical algorithms. I never liked those, I went to great lengths to try and make those easier to work with.

Now we just need to find a way to get rid of the hard coding in the kNN algorithms, that exist in the form of carefully tuned weights and states.

I suspect we'd need much longer battles for dynamic weighting to ever out-perform hand-tuned weights. I'm not even sure enemy behavior has a non-negligible impact on the optimal weights, as we generally assume. Has anyone proven a certain set of weights to be optimal against one bot, and a different set optimal against another, beyond margin of error, in gun or movement?

I don't know if that's been proved either. However, just the way that stats are gathered combined with robocode physics are going to make some weights more relevant than others.

Of course, I can choose two different bots that I know will be optimal with different kNN weights. Think a surfer vs a random-movement bot, and the weight of the rolling average between them. Or a bot that bounces off walls, and the effect of a wall-distance attribute vs a bot that smooths against walls.

The best would probably be to have multiple weighting sets, and the gun chooses the one with the highest hitrate, or possibly with some classifier.

Hah, you just beat me to posting. I was about to say something similar, that due to the limited data within a single battle, it would probably be best to select the appropriate weighting via a heuristic rather than determining it "from scratch"

I don't know that a surfer vs random movement would have different optimal weights. :-) But yes, SpinBot and DrussGT have pretty different movement profiles. I don't doubt they have different optimal weights. But I still might be wrong, and that's why we have science. :-)

Choosing among multiple weightings would mean a performance hit similar to adding Virtual Guns. So it's important to figure out how much of an impact sub-optimal weightings have in the first place. I've always thought we were fairly non-scientifically rigorous around here with some of this kind of stuff. It might be fun to try to do some rigorous testing. Actually that's one of the first ideas in a while that gets me excited about doing some more actual Robocoding.

I was referring to hard-coded bins, not hard-coded weights. Although Combat also uses runtime std. dev. based weights.

Tuning against a known population is clearly stronger than runtime estimation of std. dev. It would only be weaker if the population was constantly changing.

But having different sets of weights for different opponents seems to have potential. Forward speed is more important against rammers than wall distance. The opposite is true for non-rammers. But selecting which set is optimal against each opponent is tricky without hard-coding opponents names.

What type of distinction between bins and weights do you mean? They can have very similar effects, especially when using an interpolated VCS method. I tend to look at traditional VCS bins and kNN search w/ weightings as having a similar effect in the end, with the primary difference being that one has what is effectively a Math.Round() call in the transfer function of each dimension.

I posted that a long time ago. Only long after I learned the close relationship between bins, k-NN and kernel density.