XanderCat
Author(s)	Skotty
Extends	AdvancedRobot
Targeting	GF / Linear / Anti-Mirror
Movement	WS(GoTo) / Anti-Mirror / RamEscape / IdealPosition
Released	2011-5-20 (v 1.0)
Best Rating	#28 (v 9.6)
Current Version	9.6
Code License	GCWCD
Download

Introduction

MegaBot based on my "Xander" robot framework with pluggable guns, radars, and drives. XanderCat is the flagship of my robot fleet. It is a multi-mode robot using multiple guns and drives. It's main gun is a guess factor gun, and the main drive is a wave surfing drive (GoTo-style), but a number of other guns and drives exist for special situations. Both the main gun and drive are currently using a hybrid form of dynamic clustering. The drive is somewhat unique in that it decides where it wants to go and goes there directly (no orbiting -- though the path still is somewhat orbital in nature due to distancing -- and no wall stick while driving).

Best against: Generally a good performer; probably most notable for having effective anti-mirroring capabilities.

Worst against: Scarlet.

Arch Nemisis: lazarecki.mega.PinkerStinker 0.7 (not only because I have trouble beating that one, but because I hate it's name)

Past Version Notes: For information on versions prior to the current series, see the XanderCat/History page.

Component Chain:

Default --> Basic Radar
  |
  v
RamScenario --> Ram Escape Drive, Linear Gun
  |
  v
MirrorScenario --> Anti-Mirror Drive, Anti-Mirror Gun
  |
  v
NoBulletWavesScenario --> Ideal Position Drive
  |
  v
Defaults --> Guess Factor Gun, Direct Wave Surfing Drive

Name Origin?

Xander comes from my son's name Alexander. Cat was originally meant to be of the feline variety, as I like cats, have used the term in other software projects, and seemed appropriate. Cat could also be associated with the tractor variety, which I think also works (a tractor with a gun), and I'm fine if people make that association too.

Hybrid Dynamic Clustering?

XanderCat uses Dynamic Clustering (DC) in the sense that data about past waves is stored in a tree structure. For each wave, past data is pulled from the tree that as closely as possible matches the current situation, and that data is used to decide where to aim or drive. However, once the data is pulled, how to use it differs from classic DC. In classic DC, some form of kernel density algorithm is used to determine what to do. In the XanderCat hybrid approach, it behaves more like VCS, in that the data is logged into a factor array, and that array is surfed in the GoTo style.

Surfing Multiple Waves?

Starting with version 9.5, XanderCat now surfs up to 2 waves at a time. The general approach is as follows:

Calculate the first n best factor angles to drive to for the next wave to hit, each of which must be separated by a given amount. Each has an associated danger value.
From the end points of the n factor angles, continue to predict into the future surfing of the second wave to hit. For the second wave, only the best factor angle is used.
Add the first wave danger value to the corresponding second wave danger value for both the factor angles of the first wave. Choose which first wave factor angle to drive to by which has the lowest combined danger value.

Note the image below, where:

a = reachable factor range for first wave
b = minimum distance that must exist between first and second choice
c = first choice for surfing first wave
d = second choice for suring first wave
e = current robot position

If the sum of the danger values represented by orange lines was less than the sum of the danger values represented by the red lines, the second choice for the first wave would be used.

Linear Gun?

Originally I had both a Linear Targeting Gun and a Circular Targeting Gun in a gun array along with my Guess Factor Gun, but my Guess Factor Gun is now good enough that it works just as well by itself, so I removed the gun array and now just use the Guess Factor Gun. However, the Linear Gun still makes an appearance against ramming robots. I hate to remove the Linear and Circular guns entirely, as I spent a lot of time on them when I first started Robocode.

Version Notes

Version Ranks

Note: Best Change and Worst Change are the change from previous version against the given opponent.

Note: Starting from version 6.4 onward in the table, I am ignoring nat.BlackHole 2.0gamma and nat.Samekh 0.4, as scores against them always fluctuate rather wildly.

Version	1-on-1 Rank	Top	Best Change	% Score Change	Worst Change	% Score Change
1.0	~475 / 805	59%	N/A	N/A	N/A	N/A
2.0	386 / 806	48%	mld.Wisdom 1.0	4.18 -> 76.13	SuperSample.SuperCrazy 1.0	61.97 -> 37.00
2.1	320 / 805	40%	dz.MostlyHarmlessNano 2.1	20.90 -> 63.01	jf.Dodger 1.1	78.98 -> 41.10
3.0	148 / 805	18%	gg.Wolverine 2.0	41.08 -> 94.57	xiongan.Xiongan 1.1	100.0 -> 74.39
3.1	145 / 804	18%	zyx.nano.RedBull 1.0	53.4 -> 85.3	pulsar.PulsarNano 0.2.4	89.3 -> 64.2
3.2	116 / 806	14%	ags.polished.PolishedRuby 1	28.5 -> 83.5	rsim.micro.uCatcher 0.1	91.7 -> 31.8
3.3	115 / 806	14%	nat.Samekh 0.4	24.9 -> 64.9	simonton.micro.GFMicro 1.0	62.2 -> 39.2
3.4	85 / 806	11%	dsx724.VSAB_EP3a 1.0	67.2 -> 93.4	intruder.PrairieWolf 2.61	65.6 -> 48.4
3.5.1	94 / 805	12%	rsim.micro.uCatcher 0.1	21.3 -> 96.4	nat.BlackHole 2.0gamma	69.2 -> 42.5
3.8	81 / 805	10%	synapse.rsim.GeomancyBS 0.11	36.5 -> 49.8	mn.Combat 1.0	80.1 -> 60.8
3.9	75 / 805	9.3%	pez.mini.ChironexFleckeri 0.5	42.2 -> 64.9	rsim.mini.BulletCatcher 0.4	57.7 -> 7.0
4.0	92 / 805	11%	rsim.mini.BulletCatcher 0.4	7.0 -> 72.1	nat.Samekh 0.4	73.3 -> 45.2
4.1	74 / 805 (73.66 APS)	9.2%	nat.Samekh 0.4	45.2 -> 75.7	staticline.whiskey.Whiskey 0.6	75.5 -> 57.5
4.2	74 / 805 (73.73 APS)	9.2%	mladjo.iRobot 0.3	53.8 -> 69.9	jcs.AutoBot 4.2.1	60.1 -> 35.1
4.3	74 / 805 (73.92 APS)	9.2%	kc.mini.Vyper 0.311	33.0 -> 53.0	positive.Portia 1.26e	60.0 -> 36.2
4.4	71 / 805 (74.11 APS)	8.8%	pez.frankie.Frankie 0.9.6.1	45.9 -> 62.8	fromHell.CHCI3 0.1.4	74.4 -> 55.7
4.4.1	71 / 805 (74.11 APS)	8.8%	brainfade.Fallen 0.63	45.7 -> 65.5	jam.micro.RaikoMicro 1.44	64.0 -> 49.8
4.5.1	71 / 805 (74.25 APS)	8.8%	spinnercat.CopyKat 1.2.3	51.6 -> 69.3	synapse.rsim.GeomancyBS 0.11	54.4 -> 38.6
4.6	71 / 805 (74.35 APS)	8.8%	deo.virtual.RainbowBot 1.0	54.8 -> 69.1	jab.DiamondStealer 5	80.6 -> 61.7
4.8	57 / 803 (76.46 APS)	7.1%	ar.QuantumChromodynamics 1.2.1	69.2 -> 90.5	nat.BlackHole 2.0gamma	60.8 -> 42.0
5.0	70 / 805 (74.37 APS)	8.7%	deo.FlowerBot 1.0	53.4 -> 84.6	kcn.unnamed.Unnamed 1.21	79.1 -> 58.0
5.1	49 / 815 (78.59 APS)	6.0%	ary.SMG 1.01	35.5 -> 90.3	nat.BlackHole 2.0gamma	54.4 -> 33.0
5.1.1	49 / 815 (78.75 APS)	6.0%	rz.Apollon 0.23	69.6 -> 94.0	nat.Samekh 0.4	72.7 -> 53.6
6.1.8	59 / 806 (76.65 APS)	7.3%	cx.Princess 1.0	47.8 -> 65.1	toz.Gnome 1.1	88.4 -> 61.6
6.2	59 / 806 (76.84 APS)	7.3%	nat.BlackHole 2.0gamma	42.1 -> 67.0	stelo.RamTrackSurfer 1.2	89.2 -> 72.9
6.3	56 / 806 (77.04 APS)	6.9%	kid.Gladiator .7.2	47.7 -> 65.5	nat.BlackHole 2.0gamma	53.4 -> 33.3
6.4	51 / 806 (78.22 APS)	6.3%	dmp.micro.Aurora 1.41	62.1 -> 79.1	myl.micro.NekoNinja 1.30	77.3 -> 61.3
6.5	48 / 808 (78.70 APS)	5.9%	ncj.MoxieBot 1.0	76.9 -> 92.6	bayen.nut.Squirrel 1.621	89.3 -> 74.9
6.7	45 / 807 (79.17 APS)	5.6%	apv.ScruchiPu 1.0	57.9 -> 79.5	trab.nano.AinippeNano 1.3	91.4 -> 74.7
6.8	42 / 808 (79.69 APS)	5.2%	trab.nano.AinippeNano 1.3	72.2 -> 89.5	rampancy.Durandal 2.2d	81.5 -> 69.4
9.0	46 / 827 (79.73 APS)	5.6%	N/A	N/A	N/A	N/A
9.0.1	45 / 827 (80.03 APS)	5.4%	ary.SMG 1.01	50.5 -> 76.4	ncj.MoxieBot 1.0	95.5 -> 75.5
9.1	43 / 828 (80.25 APS)	5.2%	kc.mini.Vyper 0.311	54.7 -> 60.7	ab.DangerousRoBatra 1.3	74.0 -> 66.7
9.2	38 / 828 (80.84 APS)	4.6%	Bemo.Sweet30 1.6.1	57.9 -> 68.3	kenran.mega.Pantheist	75.3 -> 57.1
9.3	34 / 829 (81.35 APS)	4.1%	ags.micro.Carpet 1.1	76.3 -> 89.2	ary.SMG 1.01	81.2 -> 64.9
9.4	33 / 830 (81.55 APS)	4.0%	kenran.mega.Panthiest	73.9 -> 85.3	bayen.nut.Squirrel 1.621	88.8 -> 80.9
9.5	30 / 830 (81.71 APS)	3.6%	wiki.BasicGFSurfer	65.3 -> 75.5	rampancy.Durandal 2.2d	80.3 -> 67.6
9.6	28 / 830 (81.91 APS)	3.4%	darkcanuck.Gaff 1.50	43.3 -> 52.4	kenran.mega.Panthiest 1.1	89.6 -> 78.9

Version CPU Usage

All values are averages in milliseconds with the exception of those listed as Peak values, which are the maximum time in milliseconds encountered during a typical 35 round battle.

Version	Gun	Gun Peak	Drive	Drive Peak	Drive Array Select	Drive Hit Logging	Drive Predictions	Drive Adjustments	Drive Rolling Avgs
6.5	1.01	13.41, 8.77, 8.41, 7.15, 6.94	0.21	7.70, 7.61, 6.93, 6.93, 6.49	1.19 (5.53 peak)	0.02 (0.04 peak)	0.88 (6.77 peak)	0.09 (1.51 peak)	0.07 (0.89 peak)

Series 1.x

This series of robots was released while work on the Xander framework was still ongoing. Targeting and drive strategies were somewhat simple. No surfing or guess-factor targeting.

Series 2.x

This series introduced my earlist concept on guess factor targeting. Drive strategy was still simple.

Series 3.x

This series introduced wave surfing, and improvements to the guess factor gun. Version 3.0 used a modified version of the BasicGTSurfer wave surfing drive as a reference point, with all later versions in the line using a wave surfing drive of my own design. Towards the end of this line, I introduced virtual hit ratios, where all guns would provide aiming information for every shot to determine whether or not their aims would have hit the target or not.

Series 4.x

This series focused largely on drive improvements, including improvements to the wave surfing drive, dive protection, and a new Ram Escape drive. This series also improved how fire power is selected.

Series 5.x

This series focuses on using a new strategy for calculating factors in guess factor guns and wave surfing drives, surfing multiple waves simultaneously (explored by never implemented), paying more attention to robot width, improving wall avoidance, and improving segmentation selection and processing.

Series 6.x

XanderCat series 6 focuses on taking everything learned so far and evolving it into a new set of components, or at the very least, cleaning up old components or features that did not work out. Series 6 introduces the new Evolution Drive, an updated Wave Surfing drive, and new ways of handling factor arrays.

Series 7.x

XanderCat series 7 uses the first release-ready version of the Xander framework. With the knowledge gained over prior versions of XanderCat, the Xander framework has undergone a final refactoring and cleanup and is now considered ready for public use. The Xander framework can be considered as version 1.0. In addition to using the finished Xander framework, series 7 may see updates to the main drive and gun, as well as a first release into the melee competition.

Series 8.x

This series will use my own take on using a K-nearest-neighbor (KNN) approach for the main drive and gun. It may also finally introduce surfing multiple waves at once.

My KNN approach to to create a KNN Factor Array Processor that can be used in both drives and guns. I already have a FactorArrayProcessor interface built into my guess factor / wave surfing framework that it will utilize. However, instead of saving a large number of factor arrays and combining them, it will instead store a tree of data points, retrieve the closest data points to the current situation, and build a single factor array from those data points. One advantage of this approach is that is should allow for a greater number of segmenters and slices per segmenter.

Series 9.x

This series uses the updated Xander framework version 2.0. There are a variety of changes as a result. Hopefully, some buggy areas will be improved. Some new features will be added.

Version 9.0

The changes in this version from previous are fairly extensive. Changes listed are just a quick summary.