Talk:XanderCat

Congrats on breaking the 50% barrier. Seems that you have the planning of your bot on scheme, now it's just the translation into the right code. One small remark: You don't have to have 'zillions of versions' present in the rumble, the details of older versions still are available when not in the participants list anymore. Comparisons between two versions are quite easy to do like [1] . Just click on your bot in the rankings, then the details and a few older versions are shown. Good luck with your further development! --GrubbmGait 08:37, 25 May 2011 (UTC)

Thanks GrubbmGait, though I'm not sure how much praise I deserve for being officially average. :-P I'm trying out a slightly revised version today, version 2.1. No major component changes, but it modifies the bullet firing parameters, driving parameters, some segmentation parameters, and has improved gun selection. Skotty 20:43, 25 May 2011 (UTC)

Just curious, were you not using any kind of KD tree before? I haven't done any tests, but I'm fairly sure that Levy's tree is slower than Rednaxela's Gen3 tree.

For several versions now, I have been using a tree to store data, but it was something I wrote myself that was less precise and less efficient than a KD Tree (and it only did nearest neighbor, no distance or range search). I was hoping switching to a true KD Tree would improve both CPU performance and competition performance, though so far, only the former has been true.

Perhaps it is a dumb reason, but I've been trying to stay away from feeling as though I am copying other top robots, which is why I didn't look at any of the KD Tree implementations from other Robocoders. I thought about trying to write my own, but I'm already spending too much time on Robocode to reinvent another wheel just so I can slap my own label on it. Originally, I didn't even want to use a KD Tree, but after digging through my text books and other sources, a KD Tree was the perfect fit for what we are doing, so I couldn't say no.

Now if I could just figure out how to make it work better in my drive...I think I may have to drop to the level of carefully picking apart another open source top robot to figure out what they are doing that allows them to score 95 against robots I can only manage an 80 or 85 on. Those are the ones that are killing my APS.

I just released XanderCat version 9.0. It's not quite complete yet, but I couldn't stand it anymore. I had to drop it in the rumble to see where it's at. It's based on my heavily revised Xander framework. It's missing some features the previous version had, but it also adds some features (such as bullet shadows). Note that I decided to make a slight change to my package naming. My robots for the foreseeable future will now reside under "xander.cat" instead of "xandercat".

With version 9.0.1, I finally passed the 80 APS mark for the first time. Also back over 2000 on Glicko-2. Only other time that happened was way back on version 6.8. Still haven't passed my previous highest rank yet though, due to some tough new competitors since I started (Ixx, Chase, and others). Version 6.8 APS at the time was about 79.6, good enough for #42. Version 9.0.1 at the moment is 80.0 APS, rank #45. I'm convinced I can improve on this further; I just need more time to work on it.

I was here before you started, I just hadn't released any of my advanced (and unfinished) prototypes.

I ran a test, and to my surprise, my guess factor gun works as well as my circular gun and linear gun against circular and linear drivers. I've therefore reluctantly removed the gun array I was using, which included my linear and circular guns in addition to my main guess factor gun. Reluctantly, because I put so much work into developing those algorithms when I first started Robocode, so I hate to sideline them. But they are no longer providing any advantage, and at times are a little problematic. So away they go. However, my linear gun is still used against ramming robots for the time being, so they are not gone entirely.

Nene is pretty good at dodging close range linear targeting.