Using previous GFs as dimensions

I tried making a gun which uses kNN classification, in which the one dimension is shots (for data decay) and the others are the last 5 GFs the opponent went to on firing waves. Its very simple right now and I haven't tried much, but it does reasonably well (as in it doesn't get crushed, does better than random targeting) vs both surfers and simple movers. (Haven't tried against random movement yet) It seems similar to pattern matching on the opponents GFs. Any ideas?

can you show the robowiki the gun

Interesting. I think you should definitely test against RM, because surfers generally don't like anything with bullet waves, and simple movers are pretty easy to hit. Also, you might want to start with the basics (distance, velocity, relative heading, acceleration...) before you get into data decay and experimental dimensions. Good luck.

Don't worry! I am actively optimizing my more "normal" gun using standard predictors. What bots would you recommend as good test bots with strong RM? Tmservo its just a 6 dimensional KNN, 5 are lastGFs normalized to 0-1, last one is sqrt(shots)* .5. I haven't added any kernel density stuff to this tree, I plan to try that.

just drussgt, diamond, gilgalad, tomcat, xandercat, waveserpent, nene, gressuffurd,

Those are not bots with Random Movement as Straw asked for.

I'd suggest you use the reference bots from the Targeting Challenge RM, because they give you a good variety of random movements, and have been thoroughly tested against before, so the 'expected' result is fairly accurately known. You may wish to use RoboRunner to automate testing against that set as well.

Yeah, like Rednaxela said, the reference bots would work well. I'm curious, are you planning to have these two guns in a VG array, or are you just experimenting in parallel?

Seems like a neat idea. I'm pretty sure I recall prior GFs being used in kNN targeting in the past (I think I may have tried that a little), but never (to my knowledge) going to the extreme of 5 prior GFs.

Works well against active flatteners. Bad against the rest. Anti-flattener classifier.

Well, I'd imagine it might work okay against oscilator, stop-n-go, and CircleBot movements... but out of strong movements, yeah, I'd mostly expect flatteners indeed.