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Straw is discussing his development of a gun for a Robocode competition that uses kNN classification and the opponent's last 5 GFs (guess factor) as dimensions. The gun performs well against surfers and simple movers, but has not been tested against random movement. Rednaxela suggests using the reference bots from the Targeting Challenge RM for testing. The gun is still being optimized and Straw is considering using a VG array or density calculations. Voidious is curious if the 5 GFs are treated independently or as a sequence. Rednaxela suggests using relative GFs as dimensions. Straw also mentions using 10 GFs instead of 5 and using descending weights on older GFs. The gun has shown promising results against flatteners, with a similar hit rate as Diamond's gun. The performance of the gun improves with an increased value of K.
– ChatGPT
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?
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.
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.
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.
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.
If this does well vs flatteners, it would fit my strategy perfectly as I am trying to create anti surfer and flattener guns independently first. Another idea closer to pattern matching would be using the past 10 GFs of all waves, including non firing waves every tick. I am interested in the idea of looking at changes in GF between waves instead of absolute GF as Rednaxala is suggesting.
I'm curious how you're structuring this: Do you condense these 5 GFs into one attribute, or are they 5 independent attributes?
The most practical example this reminds me of is I believe Skilgannon uses his own last GF as an input to his gun to help crushing mirror movement. Using the enemy's previous movements I would guess to behave similarly to attributes like displacement distance last X ticks or time since velocity change.
My only other comments is that 5 seems like a lot. At an interval of 14 ticks, that's 70 ticks into the past, and more if you count the time it took to reach that first GF. My "distance last X" experiments have never gone past ~40 ticks, IIRC, and even that was not a strong signal.
Straw already said that there are six dimensions in his(?) gun, one for decay, and one for each of the enemy's last five visited GFs.
I'm just curious if they're being treated independently. A sequence like 0, 0.2, 0.4, 0.6, 0.8 actually might be quite close to 0.2, 0.4, 0.6, 0.8, 1.0, but if they're all treated independently, your classification won't see it that way.
If one wanted to make the ones you mention be treated similar, the way to do it would be to make the dimensions either:
GF[i], (GF[i] - GF[i-1]), (GF[i-1] - GF[i-2]), (GF[i-2] - GF[i-3]), (GF[i-3] - GF[i-4])
or perhaps:
GF[i], (GF[i] - GF[i-1]), (GF[i] - GF[i-2]), (GF[i] - GF[i-3]), (GF[i] - GF[i-4])
(where index 'i' is the most recent wave, 'i-1' is the second most recent wave, etc)
Of course, whether that would give better results than all independantly I have no idea about... and which of those ways of making it relative would be better I'm not sure about either.
Doesn't DrussGT use the opponents last GF in his targeting system, not his own? (in addition, a dimension called my expected mirror rotation at bullet hit time) It looks so far into the past because in both surfers and flatteners, (especially surfers) recent past movements are a good indicator of present movements. Fast decaying "normal" anti surfer guns do a similar thing in a different way.
Yes I do something similar in DrussGT, in two places.
1. I use the enemy's current GF as a gun attribute to help with oscillators
2. I use my predicted GF one BFT in the future as in input to help against mirror bots (and it helps a little against most other bots as well)
I'm also curious about the last 5 GFs - are they sorted and fed to the gun in ascending order, or are they used in the order they were collected? I feel that some distance/clustering method other than KNN might work best in this situation.
I agree that something other than KNN might be better, but its what I have set up right now. No sorting happens, the gun just has a dimension for the last GF, a dimension for the GF before that, etc. I found increased performance dropping data decay, going to 10 past GFs, and using descending weights on older GFs. Data decay shouldn't be necessary with this setup as if the opponent changes their movement patterns, they will change their GF sequences.
Exiting anti flattener results! The gun gets pretty much the same hitrate vs DrussGT (which can't shoot) with DrussGTs flattener allowed to turn on or forced off. Around 10.6% weighted from DrussGT's console. For comparison, Diamond's gun gets around 12.6%. The interesting thing is that the hitrate didn't go down at all with the flattener.
