XanderCat

XanderCat
Author(s)	Skotty
Extends	AdvancedRobot
Targeting	GF / Linear / Circular / Anti-Mirror
Movement	WS(GoTo) / Anti-Mirror / RamEscape / IdealPosition
Released	2011-5-20 (v 1.0)
Best Rating	#16 (v 10.22-10.28)
Current Version	10.28
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 organized into a component chain. 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 simple yet effective anti-mirroring capabilities.

Worst against: Currently DrussGT, Diamond, and Tomcat are still quite deadly to XanderCat.

Favorite Robots To Beat:

• lazarecki.mega.PinkerStinker 0.7 - because I don't like it's name.
• da.NewBGank 1.4 - because of it's name and because I can beat it quite badly due to a newbie bug in it, which is pleasantly ironic.

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

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:

1. 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.
2. 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.
3. 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 calculation is a little more complicated, taking wave distance and bullet power into account, but the basic idea is the same.)

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.

Saving Data Between Battles?

XanderCat does save data between battles, but that data is not used by the robot and does not effect performance any. It is collected solely for my own analysis. It helps me find problems and decide on where improvements are needed. I also just like statistics. Of course, I only get statistics on battles run by my own machine. Below is an example some data it collected:

Key:

1. Opponent = Opponent robot name and version
2. G1 Shots = Shots taken by the main guess factor gun
3. G2 Shots = Shots taken by the Anti-Scarlet guess factor gun
4. CD Scenario = Percentage of time the CircularDriverScenario was applied against the opponent
5. PD Engage = Number of times my bullet fire power was dropped (see v10.21 notes for what this means)
6. Flattener = Percent of total time the flattener was engaged
7. My HR = My overall hit ratio
8. Opponent HR = Opponent overall hit ratio

Opponent	G1 Shots	G2 Shots	CD Scenario	PD Engage	Flattener	My HR	Opponent HR
techdude.kombat.FlamingKombat 1.5	8	62	78.5%	0	0.0%	73.5%	3.4%
test.Fuzzer 1.0.1	449	13	0.0%	0	0.0%	60.6%	9.9%
test.Podgy 4.0	465	387	0.0%	0	0.0%	22.0%	7.2%
throxbot.ThroxBot 0.1	747	12	0.0%	0	0.0%	30.9%	5.5%
tide.pear.Pear 0.62.1	806	490	0.0%	10	7.0%	13.0%	11.2%

In addition to saving data on each robot, it can save a generic set of data about performance against the field as a group. As an example, to get an idea of early, mid-game, and late game performance, I log the number of round wins for each round. Example (to keep the table short, I left out rows for rounds 3 through 31):

Round	Wins	% Wins
0	615	88.9%
1	630	91.0%
2	613	88.6%
...
32	644	93.1%
33	654	94.5%
34	644	93.1%
Total	692

Multi-Mode Scenarios / Component Chain

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
CircularDriverScenario --> Circular Gun
  |
  v
Defaults --> Guess Factor Gun Array, Drive Array
                     |            ______|_______________
                     |           /                      \
                     |  Targeting Detector Drive   Main Wave Surfing Drive
           __________|___________
          /                      \
   Original Gun         The Anti-Scarlet Gun

Ram Scenario

This scenario applies whenever the opponent appears to be ramming. At present, this works be simply testing the opponent's distance being under some threshold. This sometimes happens against robots who are not really ramming -- this can happen due to initial round positioning, or just due to drive paths that happen to run too close together -- but in these cases there is no real disadvantage to using the ram scenario to back away from the opponent. This distance of ram detection will increase the more the ram scenario is applied. This makes the ram scenario kick in sooner and more often against the real rammers who ram consistently.

When this scenario is applied, a Ram Escape Drive is paired with a linear targeting gun. At present, this is the only place in which I utilize linear targeting. The Ram Escape Drive attempts to drive away from the enemy as much as possible, and uses turning to try and dodge some of the opponents shots (for example, it may change the direction of turn at the moment an opponent shot is detected).

Mirror Scenario

This scenario applies whenever the opponent appears to be using a mirroring drive strategy. The countering technique is to plan a drive path into the future such that it is known where to shoot in order to hit the mirrored position in the future. At present, this can be fooled by creative mirrorers that utilize a delay or position shifting.

No-Bullet-Waves Scenario

This scenario applies whenever the opponent has no bullets in the air (note: this can happen alot with a rammer, but the ram scenario happens earier in the component chain and thus superscedes this scenario). When the opponent has no bullets in the air, the Ideal Position Drive takes over and moves the robot directly to what it perceives as the ideal position on the battlefield given the battlefield size and the current position of the opponent.

I've attempted to replace this somewhat crude drive a few times, but each time resulted in a lower overall score in the RoboRumble, giving rise to the idea that this drive is embued with magical powers.

Circular Driver Scenario

This scenario doesn't affect overall performance much, but it does two things that make me feel better: first, it brings back my circular gun, which otherwise would not be in use. I once spent a lot of time on it, and would like it to be used somewhere. Second, it makes me feel better when pitted against simple circular drivers like SpinBot, where my guess factor gun still misses quite a few shots, but my circular gun can hit nearly every time.

To make this scenario as effective as possible without degrading any performance elsewhere, it is not enough for an opponent to appear to be moving in a circular path at the time a shot is taken. The opponent must have been moving (and still be moving) in a consistent circular path. Furthermore, that path must not intersect any walls before the shot would reach them. The circular movement path is predicted into the future until the bullet would arrive to check for this.

Default Gun Array

The gun array consists of several guess factor guns. This is not too computationally expensive because they are currently all using the same cached copy of data. They just use the selected data differently when building the factor array to use for aiming. The Anti-Scarlet Gun uses a certain amount of data rolling. This technique was first implemented to counter Scarlet, who at one time was trouncing my robot far worse than any other, thus the name.

Default Drive Array

The drive array consists of two drives: what I currently call the Targeting Detector Drive and the Main Wave Surfing Drive. Both drives are direct wave surfing drives. They just differ in what data they use to surf. I use the term direct to indicate how the drive moves the robot; the robot is moved at a fixed angle until it reaches the desired surfing factor angle for the current wave, in essense moving there directly in as straight a line as possible rather than using an orbital path. However, in action, it may still appear to use a somewhat orbital path, as where to move is recalculated for the same wave under various conditions -- such as when the waves are updated due to new bullet shadows, or the opponent moves too far away from it's predicted path.

Main Wave Surfing Drive

This drive uses Dynamic Clustering with a KD Tree for it's data management. A simple equation is used to determine how many nearest neighbor data points to pull, and those points are logged into a factor array to surf.

Targeting Detector Drive

This drive uses a combination of targeting detectors that are designed to detect specific types of targeting. Each targeting detector can add a data point to the factor array, with the data point weight based on how often the detector thinks it is detecting it's specific form of targeting. This system is designed to improve scores against robots that use predictable targeting (non-guess-factor), something I have had trouble getting my main wave surfing approach to handle as well as other top robots.

Bullet Shielding Protection

All main guns are wrapped by my BSProtectedGun wrapper/decorator class. It is a different animal from my original version. My original version shifted the target's position slightly to avoid perfect aim shots, a technique that works well against the basic shielder's such as uCatcher. However, it was generally ineffective against moving shielders like MoxieBot. At the same time, my normal aiming technique for my latest guess factor guns is not vulnerable to the stand-still shielders (by chance more than design). Therefore, the latest incarnation of my BSProtectedGun attempts to handle shielding using a new strategy that does not rely on shifted aims, and focuses more on protecting against on-the-move shielders.

When bullet shielding is detected, the BSProtectedGun will check the energy levels of both robots. So long as my energy level is more than the opponents, the BSProtectedGun will halt firing and wait for the shielding opponent to fire first, then the BSProtectedGun fires immediately after the opponent. This ensures that the opponent's shot is not a shielding shot. It is still possible that the opponent could fire a second shielding shot before the my shot reaches them, but in practice this hasn't been an issue. This could also fail against perfect shielders where my energy level never exceeds the opponent's, but again, in practice this hasn't been an issue. If it did ever become an issue, I would keep basically the same strategy, but start shifting target position again like in the original gun.

This strategy has an interesting yet not surprising result against MoxieBot. MoxieBot shields on the move, but the shielding is never perfect, and my robot can usually get the upperhand on energy at some point in the round. When this happens, my robot stops firing and waits for MoxieBot to fire first. However, MoxieBot in turn is waiting for me to fire before it fires, resulting in a stalemate where no one is firing. This in turn results in Robocode activating the stalemate energy drain. Neither robot incorrectly detects the energy drain as an opponent bullet, so the stalemate continues until the energy drain kills one of the robots. However, since my robot only stopped firing after getting the upper hand on energy, my robot gets the win. End result, a somewhat higher score against MoxieBot, and much higher survival.

To improve on this further, I am considering possibly using chase bullets when my energy level is less than the opponent's, which should make it harder for the opponent to shield.

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
9.7.1	25 / 832 (82.05 APS)	3.0%	rampancy.Durandal 2.2d	77.8 -> 88.3	brainfade.Fallen 0.63	64.0 -> 57.0
9.8	25 / 832 (82.12 APS, 1600 PL)	3.0%	asm.Statistas 0.1	70.4 -> 77.1	wcsv.PowerHouse.PowerHouse 1.7e3	53.9 -> 46.8
9.9.5	24 / 831 (82.37 APS, 1604 PL)	2.9%	ncj.MoxieBot 1.0	74.9 -> 86.0	cx.mini.Cigaret 1.31	64.9 -> 55.2
9.15	20 / 833 (82.74 APS, 1646 PL)	2.4%	janm.Jammy 1.0	75.2 -> 91.4	davidalves.net.DuelistMicro 1.22	72.1 -> 63.8
10.13	19 / 837 (82.98 APS, 1658 PL)	2.3%	whind.StrengthBee 0.6.4	56.8 -> 67.8	rc.yoda.Yoda 1.0.6c.fix	74.6 -> 63.9
10.15	19 / 837 (83.11 APS, 1658 PL)	2.3%	cf.proto.Chiva 2.2	61.5 -> 68.0	florent.XSeries.X2 0.17	62.3 -> 56.7
10.18.1	16 / 837 (83.25 APS, 1656 PL)	1.9%	jab.avk.ManuelGallegus 0.6	70.4 -> 82.1	dft.Virgin 1.25	73.6 -> 63.2
10.22	16 / 837 (83.56 APS, 1664 PL)	1.9%	voidious.mini.Komarious 1.88	54.4 -> 65.8	robar.micro.Topaz	74.1 -> 65.5

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	Notes	Gun Avg/Tick	Gun Peak	Drive Avg/Tick	Drive Peak
6.5	Single wave surfing, single guess factor gun.	1.01	13.41, 8.77, 8.41, 7.15, 6.94	0.21	7.70, 7.61, 6.93, 6.93, 6.49
9.13	Multi-wave surfing, single guess factor gun.	0.301	6.23, 6.21, 5.82, 5.13, 4.74	0.775	22.52, 20.01, 16.26, 16.23, 15.94
9.14 / 10.0	Multi-wave surfing, gun array with 3 guess factor guns, Levy's KD Tree.	0.182	6.13, 6.07, 5.24, 4.05, 3.93	0.544	15.24, 14.55, 14.13, 13.91, 13.78

Series Notes

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. This series saw the first introduction of bullet shadows.

Series 10.x

The "Pro" series. This series switched to using a true KD Tree for drive and gun data. However, many of the versions in this series were just parameter adjustments. The only other major feature changes were to fix the buggy bullet shadows implementation introduced in series 9 and to begin (but not fully switch over to) the Xander Painting Framework.

Series 11.x

This series attempts to add some revised functionality in an attempt to break into the top 10, or at least get very close. This series will also see another round of code cleanup to reduce code size and fully utilize the Xander Painting Framework and Xander File Utility (removing any old code that the new frameworks can take over for).

Current Series Version Notes

Version 11.0 (Roadmap)

• Use honest precise MEA for the drive and gun. Previously, I was just capping the ranges.

Version 11.1 (Roadmap)

• Use estimated opponent distancing in opponent drive predictions. Previously, I was using no distancing for opponent predictions.

Version 11.2 (Roadmap)

This version will be a general maintenance release focusing on cleanup and code size reduction. May also include minor gun and/or drive tweaks.

• Remove old paint and IO code that the Xander Painting Framework and Xander File Utility can take over for.
• Remove old functionality that had no useful impact.

Version 11.3 (Roadmap)

• Do something brilliant.