View source for 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)

Version 3.1

Interesting results for version 3.1. No real change in rank from 3.0, but using an entirely new drive. I've done away with the borrowed wave surfing drive from BasicGFSurfer and replaced it with a drive of my own design.

Despite the new wave surfing drive (which I will call a Stat Drive) which I crafted almost entirely from scratch, I think it shares a lot in common with other wave surfing drives. It's just naturally where you end up when working out the best way to drive. I really haven't tried to tweak the segmentation yet, so I think it can be further improved with a few parameter changes.

Here is what my new drive does (you will likely see a lot in common with other drive strategies out there):

• Segmentation - Much like other wave surfing drives, my Stat Drive supports segmentation. I can't remember what it is actually segmenting on right now (don't have the code in front of me at the moment). I'll add that detail later. It is very easy to change the segmentation parameters. The Stat Drive relies on a seperate component to determine the segment or combination of segments, and the segmenters can be swapped in and out easily.
• Tracking danger - Each segment has a fixed number of "buckets" or "bins" that represent the danger at a particular "factor", where a factor represents an angular offset of the robot from an original bearing of the bullet wave origin to the robot at the time the bullet was fired. When a particular factor is determined to be more dangerous, a value is added to the corresponding bin or bins around that factor. Initially when I wanted to add danger, I just added to one bin. One thing I did steal from from the BasicGFSurfer wave surfing drive was the manner of adding danger to all the bins, trailing off sharply from the most dangerous bin. I don't know why I didn't think to do this initially. Once my eyes glanced over it, it was obvious it was what I should have been doing from the start.
• Bullet Hits - The most dangerous of events -- actually getting hit by a bullet. When hit by a bullet, the Stat Drive records danger of a certain amount (lets say a value represented by the variable d) to the matching bin of corresponding segment. One tenth that amount (d/10) is currently added to all other segments, though this is just experimental; I may modify or remove that effect as I tune it a bit more.
• Wave Hits - When a wave hits (but not necessarily a bullet hit), the Stat Drive currently records one fifth the amount of danger (d/5) for the matching bin of the corresponding segment. Thea idea being that the opponents gun, likely being a "Guess Factor" gun, might be more likely to aim for that bin next time, so lets try to avoid it. This also probably needs some fine tuning. Part of the same experiment as with bullet hits, adding one tenth of that amount (d/50) is added to all other segments.
• Wall Avoidance - While I wrote it from scratch myself, wall avoidance right now is doing pretty much the exact same thing as the "wall stick" approach. I have some ideas that would be fancier, but the "wall stick" approach works for now.
• Rolling danger - Rolling danger is the idea of removing danger previously added from bullets or waves that are over a certain age. The Stat Drive is not doing this right now, but I'm planning on experimenting with the idea.
• Figuring out where it can get to - One of the first steps to avoiding a bullet wave is determining how far you can go in each orbital direction before the bullet hits. At first I used some crude approaches to this with the Stat Drive, but they just won't cut it if I want to be really competitive. I now have it predict our position into the future, taking pretty much everything into account (turn rates, acceleration/deceleration rates, wall smoothing, etc) to make the prediction as accurate as possible.
• Figuring out where to go - once we know how far we can go, we have to decide where in that range we want to go. I'm experimenting with a few approaches. For now, it looks for the bin with the lowest value and heads there.
• Figuring out how to get there - Once we know where we want to go, we have to figure out how to get there. This seemed simple enough, but one problem I ran into was overshooting the target and being in the wrong place when the bullet arrives. This problem turned out to be significant in my testing. So I had to do additional work to ensure that if I will reach my target before the wave hits, I slow down before getting there so I land right on target. This sounds easier than it actually was to implement.

Where to go from here?

Performance was roughly equal in the rumble to the BasicGFSurfer drive. I need to tweak the segmentation approaches and parameters. I need to tweak the manner of adding danger. And I need to play around with rolling danger to see what effect that has. Once that is done, I don't believe I will make any more changes to the Stat Drive or it's use in XanderCat.

I may employ other drives in combination at some point using a "drive selector", an ability that is built into the Xander framework. For example, I would like to build a drive and gun built specifically for "mirror" bots that mirror their opponents drive; the drive selector would switch to these components whenever a mirror bot is detected.

Outside of drives, my Stat Gun is still a bit crude. I know I can improve there. And then I also have a few other little tricks up my sleeve I would like to try when I have the time.

It's neat to see you taking such a systematic approach with robocode! If I may a few suggestions:

• If I understand correctly, you add d/10 danger to every bin (except the one that hit you) rather than (in addition to?) adding bin-smoothed danger to each bin. I don't think this will help anything. The bin-smoothed danger should be enough.

• Logging hits from every wave, regardless of whether the other robot hit your robot, will give you a flatter movement; however, you may not actually want a flatter movement, even against GF guns. Before wave surfing was invented, I imagine that GF guns didn't roll their averages very much. Because of this, a flat movement will be worse against these guns. Rather than giving the enemy a flat movement to shoot at (which will make their targeting very close to random), you should move to the same GF repeatedly. When they hit you there, you know there is a peak in their stats, so you move somewhere else, and hopefully they keep shooting there for a while, allowing you to dodge bullets. Did I explain that well?

(I had thought of doing something like that but didn't for the reasons described above. It sounds rather similar to YersiniaPestis, but without the adaptive weighting of the flattener.) --AW 19:52, 6 June 2011 (UTC)

Nice job! Implementing Wave Surfing correctly can be a huge undertaking. My first suggestion would be to try just disabling the "every wave" logging of hits. You're right this should make you more unpredictable to learning guns. What most of us have found is that straight dodging from bullet hits actually works better against the vast majority of guns - only against the best guns does a "flattener" (what we call that mode) help. But more importantly, a flattener also destroys your scores against simple targeters.

I see you're getting 80% vs Barracuda and 88% vs HawkOnFire. Those could both be over 99% with no segmentation. My best advice would be to work on distancing, dive protection, and ironing out bugs until you can get that before trying to refine other aspects. Working on other stuff will only make it harder to fix the core stuff, and you may have to re-tune everything anyway once it's fixed. (Just turning off the flattener may go a long way!)

Good luck! --Voidious 20:37, 6 June 2011 (UTC)

Thanks for all the suggestions. I've been intentionally trying to come up with a lot of the ideas and code myself, as that makes it more rewarding for me. On the down side, this has made it harder to learn all the terminology used by the Robocode community. Not until both of your comments did I know what a "flattener" was, though I had seen the term pop up here and there. I hadn't spent too much time thinking about what the effects of it would be against different opponents, but your comments give me food for thought. I actually have my system set up now to run different configurations of my robot against a test base of robots, so I am now at the point where I can see some real results rather than just trying to deal with the theoretical. I'll try turning off the flattening and see what happens. Skotty 22:18, 6 June 2011 (UTC)

I assume by "distancing", you mean trying to keep my robot a reasonable distance away from the opponent. I was thinking maybe try to refine my drive path so that it will move slightly away from the waves if the wave origins are too close, and possibly favoring non-smoothing directions when near a wall and the calculated danger in each direction is similar. "Dive protection" as I understand it is not driving towards the enemy excessively; I don't see how this would happen except to a limited degree when wall smoothing, or on startup before bullets start flying (at the moment, when no bullet waves are in action, XanderCat will just move in a straight but wall-smoothed path, causing it to circle around the edges of the field; I should probably change this to make it move into a more desireable position before bullets start flying, rather than relying on chance). Skotty 17:46, 7 June 2011 (UTC)

Version 3.2

Had a bad night of tweaking where everything I did seemed to make my robot worse. I've turned off the flattening but I don't think I have a proper test bed of opponents to determine what kind of effect it might have. It did increase the score on Barracuda and HawkOnFire, but only to the about 90%. I did, however, finish some new anti-mirror components. PolishedRuby 1 is soooo dead. :-D I haven't tested against any other mirror bots yet, but my tests against PolishedRuby put a big smile on my face. As you might guess, when mirroring is detected, I have a drive and gun that work in tandem, where the drive plots a semi-random route in advance that the gun can process to fire on the mirror of the future positions. Awesome. My framework makes it easy to add it on to any existing gun combinations. It's not perfect, but it gets the job done, and might can be improved a bit more. With the mirror components active, my gun hit ratio on PolishedRuby jumped from something very sad up to a wicked 70%.

I've got my score on Barracuda up to the low 90s, and HawkOnFire up to about 98%. I've disabled the flattening, and I've implemented a new drive that takes command on the first few moments of a round to try and obtain better positioning before bullets start flying. Also, my StatDrive will now change it's angle a little to back away from waves when it deems itself too close to them. I also have the anti-mirroring operational. I was going to try and update my StatGun for this version (3.2), but I think I may hold off on that until version 3.3. I want to see what effects the changes so far have made.

Rumble Results

Fascinating results in the rumble. Despite winning fewer rounds than version 3.1, version 3.2 ranks about 30 places higher. Presumably by beating the simpler robots by quite a bit more than previously. Lots of variations in the PBI. I'll have to play around with some of the ones version 3.2 performed poorly against. I'll probably come up with a way to turn the flattening on and off automatically, which I think some of the other robots do. Only other thing I can think of to do for driving is to put some work into not running into the opponent robots, which is something I have ignored previously. AntiMirror components rocked.

Next version I will update the StatDrive. I know of a couple of ways it could be reasonably improved. And after that? Not sure...

My suggestion is that you need to up your score against Barracuda - there are still lots of points to be gained there. Until you are getting 99+% you are losing points due to not getting far enough away. If you watch your battles against Barracuda note every bullet hit and think about what your bot could have done differently to avoid it, be it reversing, being further away, not being against a wall, etc and then code something to get it to (not explicitly, but generally) avoid that situation next time. I'm not sure how your drive system works but in my bots I modify the desired angle towards the orbit centre proportional to the distance from the orbit centre, so as it gets farther away it moves towards the centre, and as it gets closer it moves away. Anyways, some food for thought. --Skilgannon 08:58, 8 June 2011 (UTC)

Were you looking at version 3.2 or 3.3? Version 3.2 gets about 93% against Barracuda. Yeah, it could be better. Version 3.3 in the rumble only got 80% against Barracuda. I can't explain that. I've run 3.3 myself against Barracuda many times, and it always gets around 93%, same as version 3.2. Ignoring that anomaly, where is that other 7%? It may be from collisions. Right now, XanderCat ignores collisions with the opponent, and can get stuck rammed against them. Also, it may be from getting too close when wall smoothing. I had mentioned the possibility of moving away from the wall in cases where danger each direction is similar in order to not get jammed against the wall as much, but I haven't implemented that idea yet. Skotty 14:21, 8 June 2011 (UTC)

I'd suggest hunting for bugs really. Even 98% against HawkOnFire, is low enough that I think that notable surfing bugs could be likely, (and having a large hidden influence against other bots). It should be possible to get to 99.9% or so if the surfing algorithm is reasonably precise and the bugs of implementation are worked out. --Rednaxela 14:48, 8 June 2011 (UTC)

Version 3.4 moves up the ranks, but still gets 98% on HawkOnFire and 95% on Barracuda. I believe this is due to not having any dive protection when wall smoothing (I do employ distancing, if I understand correctly what this is -- attempting to back away some when in too close -- but this doesn't help if you are getting smoothed into a wall). I'm still working on a good solution for dive protection near walls. I tried a few things when developing version 3.4 but haven't come up with something that improves performance yet. Perhaps for the next version. -- Skotty 17:16, 9 June 2011 (UTC)

A quck note for you in relation to ucatcher. That bot is uses bullet shielding. Basically, the better you shoot at it, the better chance it has to deflect your shot. Not too hard to detect when it happens either, just check the bulletHitBullet event and see if it happens a bunch. If so, don't aim for the center of the bot, but an edge of it instead. --Miked0801 23:31, 10 June 2011 (UTC)

I started doing some research on my shielding success rates. I started with a small set including Virus, Seraphim, Hydra, Crusader, and Engineer. I did a first test run of 20 seasons. Shielding success occurred on 20/20, 19/20, 20/20, 20/20, and 13/20 seasons respectively. I know why it's not perfect for Engineer, but the other 4 are the mystery. In the RoboRumble, my success rates on the first 4 are currently 5/8, 5/8, 5/8, and 4/7 respectively.

None of the Rumble losses were run by my machine. But all of them were run using client 1.7.3.0. Losses only occurred when run by Voidious and DivineOmega. I'm guessing there is something common about the machines used by you two that is key. Are you both perhaps running under Linux? Also, what version of Java? I need to replicate one of your machines as closely as possible to explore the problem further.

Maybe their clients are skipping turns. Missed scans hurt bullet shielding a lot.

If they want to check that, they sort-of can. It only reports on the last battle that it happened, but if my robot has any skipped turns, it will write a file xander.cat.XanderCat_SkippedTurns.txt file into it's data directory that has the number of skipped turns printed in it. It's not as useful as it could be since it just reports on the last battle that it happened on, but could be worth a check.

Are the tests you run also using 1.7.3.0? (BTW, I'm doing some major bullet-shielding-suffering at the moment...)

Yes. I run 1.7.3.0 as well.

My rumble clients are on a Core i7-3770 running Ubuntu 12.04 (32-bit), and I think OpenJDK 6 (will check when I get home). Now that you mention it, I recall a similarly huge discrepancy for the original BulletCatcher on an old AMD Ubuntu machine I was using as a rumble client (couldn't find the discussion though). I chalked it up to the different JVM or even the CPU and ended up just retiring the machine as a rumble client, since it was a fraction of my overall CPU power.

My first guess is that when two bullets are close enough to parallel, one JVM's Line2D says they intersect and another doesn't. I actually have Ubuntu 32-bit, 64-bit, and Windows 8 64-bit all on that same machine, so I should be able to do some decent tests for you with only the JVM or OS as a variable.

Found the discussion: Archived_talk:User:Voidious_20110909#Wierd_scores

When the opponent is moving, my robot stands perfectly still (it rotates but doesn't move position). Thus, I haven't verified it, but the two bullets should be perfectly parallel, and this is likely handled differently on different JVMs as Voidious was talking about. However, when both robots are standing still, my shielding shot will miss, so my robot will move slightly when firing a shielding shot against a stationary opponent. I haven't had enough time to figure out what causes this, but I wonder if the answer will give any further clues. I did it this way because it worked, but more investigation is required for me to figure out why.

I could play around with moving slightly for every shielding shot (and moving back right after the shot). I tried this briefly, but found standing still to be more reliable on my machine. But maybe I can tweak it to where it works as well as standing still. If I can, I bet it would avoid the problem.

I will likely need a few more days to play around with this further. In the meantime, if Voidious has time to try out the other JDK, that would answer another piece of the puzzle.

Standing still works because the enemy gun rotation happens after the bullet is fired, so they shoot from a position of 1 tick ago but use their aim from 2 ticks ago. So if they are moving their angle to you changes and they don't quite shoot at your centre, meaning that you can get your bullet line to intersect with theirs because your bullet comes from your centre (which they aren't aiming for). This is also why it stops working when they stand still, because their last position relative to you lines up with their current position relative to you so you end up shooting parallel.

I'm trying to figure out why my super-advanced precise circle-line intersection methods are failing so horribly at getting shield hits. I get one every now and again, but nothing like what I should be based on the maths and shield size I'm calculating, and nothing like the 3/4 of bullets that a simple linear projection + bullet power adaptation was getting.

I would recommend against using Seraphim in a test bed. It is a very buggy robot. So much so that I think it sometimes acts differently in a rumble environment then in a test environment.

But also some versions of Seraphim if I recall add a minor variance to its gun heading it detects it is against a bullet shielder. Is the version your using the same as the one in the rumble? (I expect so, but it cannot hurt to ask.)

After some testing, maybe we can come up with a more consistent implementation for checking bullet collisions / line intersections in the Robocode engine. Here's the relevant code snippet: [1] (line 76 calling line 113).

Here's one that gets rid of the division which I expect is what blows up on poorly conditioned problems:

	private boolean intersect(Line2D.Double line) {
		double x1 = line.x1, x2 = line.x2, x3 = boundingLine.x1, x4 = boundingLine.x2;
		double y1 = line.y1, y2 = line.y2, y3 = boundingLine.y1, y4 = boundingLine.y2;

		double dx13 = (x1 - x3), dx21 = (x2 - x1), dx43 = (x4 - x3);
		double dy13 = (y1 - y3), dy21 = (y2 - y1), dy43 = (y4 - y3);

		double dn = dy43 * dx21 - dx43 * dy21;
                double dn_sign = Math.signum(dn);
                double dn_abs = dn*dn_sign;

		double ua = (dx43 * dy13 - dy43 * dx13) * dn_sign;
		double ub = (dx21 * dy13 - dy21 * dx13) * dn_sign;

		return (ua >= 0 && ua <= dn_abs) && (ub >= 0 && ub <= dn_abs);
	}

It might even be faster, divisions are about the same speed as sqrt.

Given that it is using it's own code to determine intersection rather than a JVM method, I wonder if the discrepancy between systems is actually in the data stored in the line objects.

I thought maybe I could tweak it using a small amount of movement to make it work on all systems, but so far my attempts have degraded shielding performance unacceptably. One bit of good news -- I tested and found out that Robocode security does not prohibit robots from reading System properties, so if I can figure out how to correct (at least partially) for the problem on other OSs or JVMs, I can test for them and just make those changes on the appropriate systems.

When I experimented with bullet shielding, my bot calculated minimum and maximum angles which would hit an incoming bullet, and only shoot if the difference between angles was above a threshold. It was there to work around floating point calculation errors, which translate into "parallel" bullets.

If the difference is below the threshold, then moving sideways helps increase the difference. Near 100% bullet shield against TrackFire. But against moving opponents and/or weak powered bullets, sometimes my bot moved until it crashed on the wall, never finding a good angle to shoot. And this is where my experiments are stuck right now.

Tonight I ran a test on one of my servers in my basement. The OS on it is Fedora 13. I installed Open JDK 6 (command su -c "yum install java-1.6.0-openjdk-devel"). Once installed, it reports itself as:

OpenJDK Runtime Environment (IcedTea6 1.8.8) (fedora-51.1.8.8.fc13-i386) OpenJDK Client VM (build 14.0-b16, mixed mode)

I ran a number of battles and have yet to encounter any shielding failures using XanderCat 12.2. But I need to do a more thorough test to be sure. Tomorrow I will set up RoboJogger and run a bigger series of tests.

Just checked and I'm also on OpenJDK 6:

java version "1.6.0_24"
OpenJDK Runtime Environment (IcedTea6 1.11.5) (6b24-1.11.5-0ubuntu1~12.04.1)
OpenJDK Server VM (build 20.0-b12, mixed mode)

Sorry I didn't get to any real testing today, but I will tomorrow. Now I'm really curious to see if I see the same in another JVM or in Windows...

I see that yours is the Server VM also. I wonder if that makes any difference.

In case it's easier just to copy it from here -- here is a challenge file with 3 opponents that XanderCat 12.2 is nearly perfect against on my machine but only maybe 50 percent successful against on machines that exhibit the performance anomaly.

Shielding Vulnerable
PERCENT_SCORE
35

Shielding Vulnerable {
    apv.test.Virus 0.6.1
    kc.serpent.Hydra 0.21
    trab.Crusader 0.1.7
}

I finally got my precise intersection with bullet shadows working for bullet shielding, so Voidious, if you're going to run some tests to see if bullet collisions work on your machine, give this a try as well: https://dl.dropbox.com/u/4066735/jk.precise.BulletShieldTest_1.0.jar I'm theoretically getting around half a pixel of shield width, so if that doesn't work then there is something seriously wrong.

Hi mate.

I was in the mood to check XanderCat 12.2 on my system and maybe it can help you to detect some uncertainties.

Mac OS X 10.6.8

java version "1.6.0_37"
Java(TM) SE Runtime Environment (build 1.6.0_37-b06-434-10M3909)
Java HotSpot(TM) 64-Bit Server VM (build 20.12-b01-434, mixed mode)

Robocode version: 1.7.4.4
CPU constant: 5426594 nanoseconds

Result vs Virus (png) Last Round Log vs Virus (png) Round start log vs Virus (log)

I started a couple of runs and it looks like almost 50% failed because of massive skipped turns if the round starts. This happens if the client runs on low speed and as well on full speed. If I put the client on debug=on, everything works fine and XanderCat win all rounds like expected. Because of the skipped turns XanderCat misses quite a few bullet catches and decides to move with a different movement - from that point he is almost ever doomed to loose some rounds. I hope it helps a little.

BulletShieldTest 1.0 worked so far very good on my system. He is lost against all 'sloppy' guns (HawkOfFire,SpinBot..) but against well coded targeting he has a quite impressive performance. Of course, he has still some minor flaws but i guess that is not unusual for a test version.

All in all i would say there is no difference in how the JVMs handle intersection but there may be a difference how they handle garbage collection and therefor have a different skipped turn behavior.

Man, I wished I had more time these days :( - take care

Thank you, Wompi. That is very helpful. From the round log, looks like there are occasional skipped turns (most likely due to the wave surfing drive and guess factor guns, but these sit idle when bullet shielding is active, and bullet shielding is much less processing intensive). However, the first round where most things are initialized is the worst.

So it's probable the problem isn't with the JVM doing the shielding calculations but rather how much time each system is allowing and how long it takes my initialization code to run (which I can work on, but it may also be true that different JVMs take different amounts of time for different types of initialization).

Looking at the info provided by the run time loggers, the averages between my system and yours aren't all that different, but the peaks shown on yours are an order of 5x what they are on mine. The radar peaks are even worse, which is weird. A typical greatest peak for my radar on my system is 0.25 trialing down to less than 0.1 by the 3rd peak, whereas on your system they start at 9.9 and are still above 0.8 by the 5th peak. (note: peaks are over the entire however-many round battle, as are the averages)

Both your system and Voidious' system are using the Server VM, and that makes me suspicious. I need to try out a Server VM and see what happens.

It should be noted that if this is the problem for Voidious, DivineOmega, and perhaps others, it is affecting overall performance (in the first round at least) and not just on the bullet shielding. If I can find a way to fix it, overall rumble performance could improve some.

I just tried the Server VM on mine but didn't see anything change.

Running some tests now. Starting with 20 seasons, single-threaded on the Ubuntu/OpenDJK setup. 10 seasons in and see it failing a lot vs Virus (~76%), rarely vs Crusader (~90%), and working well vs Hydra (96%). I'll post more details and the full RoboRunner logs when I've got some more data, and try it through the UI to see what I can tell about skipped turns.

If it is a skipped turns issue, I know there is one trick to put heavy initialization stuff in a static block and it won't count against your CPU time. I really hate Robocode's skipped turn setup, though I think limiting the CPU time in general is great. I'm definitely taking a different approach in BerryBots. It's probably too late to completely change it in Robocode, but we could at least look at an average over the last 5 or 100 ticks and penalize based on that instead (which is part of what I'm going to do).

Wow, ok.. So 4 threads was slightly worse overall, but 20 seasons is hardly enough to cover the margin of error. Now testing on Windows, and at 98 overall score after 8+ seasons. This is Oracle Java 7. Also interesting to note that the CPU constant was set to about 5 ms in Windows, vs 7 in Linux. Also this is all on Robocode 1.7.4.0, sorry about that, but I don't think it makes a difference.

Ok, still want to test on Ubuntu 64-bit and with the Oracle JVM on Linux, but here's a bunch of data for now.

config	Virus	Hydra	Crusader	Total
Linux/OJDK/1 thread	76.14 +- 8.29	95.93 +- 0.37	89.26 +- 5.17	87.11 +- 2.66
Linux/OJDK/4 threads	81.45 +- 7.6	82.77 +- 6.51	90.91 +- 5.14	85.04 +- 2.92
Windows/1 thread	98.89 +- 0.25	96.09 +- 0.33	98.67 +- 0.34	97.88 +- 0.17
Windows/2 threads	97.18 +- 1.91	96.31 +- 0.35	98.92 +- 0.3	97.47 +- 0.48
Windows/3 threads	95.4 +- 3.19	96.39 +- 0.23	88.88 +- 5.49	93.56 +- 1.55
Windows/4 threads	98.66 +- 0.31	92.2 +- 3.5	92.42 +- 4.31	94.43 +- 1.41

It's worth noting that I feel comfortable running my own benchmarks 6-threaded on this machine and I usually run 4 RoboRumble clients at a time.

Thank you, Voidious, for all the data. Don't forget to take a peak at skipped turns under Linux/OJDK. It gets printed to the game log, and you can also check what is printed in the SkippedTurns text file it writes to the data directory.

For now, I'm going with the assumption that I need to target reducing initialization overhead due to skipped turns. Once I have that done, I can prepare another version of XanderCat, which if I'm lucky you will be willing to run through your Linux/OJDK setup again to see how it changes things.

So, upon not getting to the bottom of this, I stopped running my rumble clients. Where are we with the discrepancy in shielding success on different systems? I don't particularly mind keeping my clients off for now, since I'm not submitting bots, but I do have quite a bit of CPU power I'd be happy to contribute.

I also still want to test with the newer XanderCat, Ubuntu 64-bit, and a different JVM on Ubuntu, but that requires Voidious-time and not just CPU-time. :-)

Not much new at this point. I greatly reduced my processing overhead in XanderCat 12.3+ in an attempt to improve on the problem (nearly cut average and peak processing times for targeting and surfing in half; see Version CPU Usage section of my XanderCat page; noting that my CPU constant is around 10ms), but since the problem seems linked to garbage collection, I don't think it will result that much of an improvement (I'm guessing far fewer skipped turns in later rounds but still a bunch in the first round, which will still break shielding). I may be able to reduce the amount of garbage collection by trying to eliminate as many local method-scope variables as I can, but that would be a lot of work for something that will likely make the code less readable and I don't even know for certain if it will help. Thus I haven't tried it yet.

Wompi indicated that using -XX:+UseConcMarkSweepGC to use the concurrent mark and sweep garbage collector eliminated the problem, but I don't think anyone was sure whether it was a good idea or not to use this as a solution. Not sure if it could cause other problems.

For now, I don't mind if you restart your clients. XanderCat 12.5 already has over 10K battles and I don't intend to replace it with a newer version for awhile.

Mine is a rather complex set of conditions, some that can happen on and off and some that can effectively disable shielding for the rest of the battle.

I'm guessing you are more interested in the conditions that that turn it off for good. That determination takes into account total bullet shielding misses, consecutive bullet shielding misses, average damage taken per shielding shot (presently it assumes all misses will result in damage, but I've been contemplating changing this to only when real damage occurs), overall shielding success ratio (total shielding hits / total shielding shots), and round number (some things more lenient on earlier rounds).

More temporary on and off conditions include stuff like rolling average opponent fire power, opponent distance, round time, whether or not the opponent keeps not firing first, and whether or not a shielding shot was just missed.

If you want to see specifics, you can dig into my code and look at the big nasty condition starting on line 123 of BulletShieldingScenario. It just might be the biggest condition statement I've ever written. I need to do more research to figure out how often any of those conditions are actually triggered to fine tune it all and order the conditions to minimize processing required, but I don't expect a lot changes, other than cleaning up the class so it's not so messy.

Here I am going to post information on CPU performance of configuration, construction, drive, gun, and radar. Configuration is one time setting of parameters at beginning of first round. Construction is construction of the scenarios, drives, guns, etc. and loading them into my component chain, also a one time event at beginning of first round. Load stats is loading the previous set of statistics from disk so they can be updated and written back out at the end of the battle. Drive, gun, and radar times are averages and peaks over every tick for the entire battle.

In all I ran averages against 10 seasons.

12.2 is XanderCat 12.2, while 12.3 is the development version of XanderCat 12.3 with whatever CPU performance improvements I can make. Originally I was going to focus on trying to improve initialization, but since turn 0 apparently wasn't getting skipped, I decided to focus on drive and gun improvements instead.

	12.2 Normal	12.2 Shielding	12.3 Normal	12.3 Shielding
Opponent	Tron	Virus	Tron	Virus
Configure AVG	0.454	0.478	0.459	0.469
Construction AVG	1.304	1.353	1.306	1.310
Load Stats AVG	3.730	3.686	3.690	3.321
Drive AVG	0.478	0.031	0.299	0.016
Drive P1	12.51	5.25	7.34	3.82
Drive P2	11.91	4.70	6.95	3.18
Drive P3	11.46	4.55	6.81	2.99
Gun AVG	0.465	0.153	0.267	0.082
Gun P1	7.12	7.17	5.23	5.84
Gun P2	6.03	5.13	4.44	4.09
Gun P3	5.65	4.26	3.77	2.66
Radar AVG	0.0019	0.0018	0.0018	0.0018
Radar P1	0.10	0.28	0.07	0.35
Radar P2	0.06	0.14	0.05	0.05
Radar P3	0.04	0.04	0.03	0.04

I hadn't thought about it previously, but I can't help but wonder if the loading of battle statistics is one of the bigger problems with skipping turns at the beginning of the first round. On my system, the CPU hit is not all that dramatic, but it could be worse on other systems. Maybe I could defer loading the battle stats until the end of the battle? They are not really needed until the end. How much time does Robocode give at the end of battle for final processing like writing to files?

I can think of 2 factors which can mess up skipped turns. Dynamic overclocking and excessive amounts of client instances.

- Dynamic overclocking changes CPU speed based on load. If the Robocode engine CPU constant is calibrated for an overclocked CPU, then in the beginning of a battle, the reduced clock will provoke skipped turns.

- Too many client instances make them interfere with each other. Sometimes clients use 2 threads, sometimes only 1. When they use 2 and there isn't any idle core, they steal CPU from another instance and the slower processing speed can provoke skipped turns.

You get extra processing time on ticks that have disk access, but I'm not sure how much.

Some thoughts...

• For what it's worth, I tried to generate different CPU constants based on whether it was dynamic overclocking or not, but wasn't able to. Maybe the trigger threshold is well below what a single Robocode instance or the CPU constant calculation uses on my machine.
• While Robocode does use multiple threads - you might see 50% on two cores or 100% on one core - it only uses 1 at a time, so I'm not sure it's ever really stealing CPU.
• Since a lot of modern multi-core processors include hyperthreading, there should be some extra buffer such that using 1 Robocode per core is ok.

To me, the bigger problems are:

• Measuring CPU time for a single tick in nanoseconds is not nearly as accurate as we need it to be.
• There's always other system stuff that could use some CPU. Combine with the lack of accuracy and it's really dangerous to penalize for taking too long over a timeframe of just one tick.

Hmm, maybe I'm wrong and System.nanoTime() is accurate enough... [1]

I don't know, I agree that dynamic overclocking is theoretically a concern, but from what I can remember, skipped turns have been quirky since long before we all had huge multi-core machines with hyperthreading and dynamic overclocking.

Quirky like skipping more turns vs an opponent that uses a lot of CPU (but doesn't skip turns!), or skipping lots more turns in the first 50-100 ticks of a battle. Obviously you do some initialization in the first tick, but shouldn't the subsequent ticks be mostly unaffected by that? Seems like that was never the case and still isn't. I guess garbage collection being outside of the scope of Robocode's vision is the most likely culprit for all this. But it's pretty frustrating, in any case.

That's a good point I wasn't even thinking about. Looking at the log Wompi posted again, I can see that all setup was completed on turn 0, and turn 0 wasn't skipped.

Voidious -- did you check to see if XanderCat appeared to be skipping a lot of turns on your Ubuntu/OJDK machine?

I can try to improve the CPU performance of my wave surfing drive and guess factor guns, and that might help, but it doesn't really explain the huge number of skipped turns on the first round. In the meantime, if skipped turns is a problem on Voidious' machine, I suppose for now I could just modify my bullet shielding system to try to account for skipped turns.

Remembered a 3rd possible cause of skipped turns. The JVM executes all code in interpreted mode for a while until JIT compiler kicks in. And at least in Sun/Oracle JVMs, the default JIT mode, client or server, varies with OS.

Hmm... frustratingly, XanderCat seems to have a higher success rate vs Virus (the most problematic on my system) when running through the UI. I did catch one 56% score with only 2 skipped turns, though, which raises some doubt that skipped turns are the issue (or only issue). Running some more single-threaded seasons with RoboRunner now, in case it's something like a fresh reboot helped (after testing in Windows earlier).

If you want to play around with it, I went ahead and released XanderCat 12.3. It's basically the same as 12.2 but I fixed some CPU performance issues. The main drives and guns execute quite a bit faster now with much lower peaks. Maybe it will make a difference...maybe not.

I thought once about Robocode being more multi-thread friendly if the engine called Thread.yield() in strategic places, like between ticks. But I never posted this suggestion in sourceforge.

This way, most processing outside a Robocode instance would be done between ticks and not interfere with turn skipping.

About dynamic overclocking, here I delete config/robocode.properties and run a single instance, which will measure CPU constant at minimum possible speed. Then I copy the file to all other installations. Tried to disable dynamic overclocking as well, but my laptop doesn't have the option in the BIOS.

Hi mates.

We had once a quite long discussion about skipped turns (Skipped Turns) and I remembered this gc-tuning page. I played around with some of the gc settings and it generated a very different robocode experience. To sad, I don't have the time right now to provide some serious tests but maybe it gives someone a hunch to find some appropriate settings for the gc.
One other thing, do you look out for 'hidden' skipped turns or just take the skipped turn events?
My overall guess would be, that initialization is not a big problem and could easily detected if you spot skipped turns at the start of the round (I remember DrussGT had once an issue where he skipped the first 10+ turns). It is more likely that bots generate a bunch of initialization objects and this bunch of objects get garbage collected a couple of turns later (lets say turn 30+) and that would be the time where it hurts most. Of course this is just a wild guess and i could completely wrong but it could be one explanation why quite a few bots have issues with skipped turns mainly within the first round.

I think another reason the first round is difficult is that the JIT hasn't yet optimized code, but also a few ticks later the JIT figures out which code needs optimization and then starts running the compiler in a parallel thread. This competes with the robot time, so not only do you not yet have optimized code, but the time is also being shared with the JIT compiler. This would also explain skipping turns starting a few ticks in instead of on the first turn. Also, the more code you have that gets run regularly, the more the JIT will try to optimize, so it will take longer and possibly cause more skipped turns.

Well, I couldn't hold it and had to run some tests.

I tested all three collectors on XanderCat 12.3 vs Virus and Diamond vs DrussGT

-XX:+UseSerialGC

DvsD: both bots dropped turns like crazy and DrussGT almost ever won with 64% APS
XvsV: XanderCat dropped constantly turns over all battle rounds (sightly more on the first two rounds) and lost quite a few rounds against Virus

-XX:+UseParallelGC -XX:+UseParallelOldGC -XX:MaxGCPauseMillis=1

DvsD: both bots dropped even more turns than with the SerialGC - DrussGT still won with 64+% APS
XvsV: XanderCat dropped a lot turnes in the first round and some in the second - all other rounds had no skipped turns (because of the crummy first round he took the wrong movement and lost a bunch of APS vs Virus)

-XX:+UseParallelGC -XX:+UseParallelOldGC -XX:MaxGCPauseMillis=30

DvsD: less skipped turns for both but still to much - DrussGT still on 64% APS
XvsV: no changes to 1ms pause

-XX:+UseConcMarkSweepGC

DvsD: DrussGT drops a turn here and there - Diamond still drops a lot turns but with a lower frequency - DrussGT wins with 50-55% APS
XvsV: XanderCat stops dropping turns at all (just a few here and there) and wins against Virus with 99%

I also changed some other ratio settings but could not see any visible changes to the overall behavior. To me the concurrent collector looks quite promising and I think I play a litlte with the CMSIncremental.. options to see if there is still room for some improvements.

Very interesting. Thanks again for all the testing. Even if the issue can be fixed by GC settings, maybe I should look to see if I can make my robot more environmentally friendly and stop producing so much garbage? My first thought turns to the way I pick data out of my KD trees (because I sloppily move all data points from a MaxHeap into a List in order to stick to my existing interface, and that List gets created and thrown away on every tree read), but I'm not sure that would help on the first round issue.

Wow! Great research here. I wonder if you enable incremental mode for the concurrent mark sweep GC using -XX:+UseConcMarkSweepGC -XX:+CMSIncrementalMode how that would work. Also, the concurrent collector uses additional cores to do the work, so might not work so well unless there are cores free. Of course, designing a bot so that it is resistant to skipped turns, and uses little CPU and memory, is probably the most important aspect of all.

Uh, I wouldn't change anything in this direction. It was just a quick test (<10 seasons) and I doubt it solves anything. To me it just shows a dependency and might worth further test. I'm still convinced that another skipped turn handling (like Voidious suggested) would be far better. Maybe raising the cpu variable for rumble clients would be a workaround - but this would need that everyone develop his bots within the normal cpu range and not abuse this workaround. Probably not a good idea but like many others I get more and more annoyed by the skipped turns because it complicates even very simple tasks.

I did a little reading on the garbage collector looking for information on how I could reduce my garbage collection footprint. Short of stripping things out of my robot permanently, I did come up with a few things I could potentially do to reduce the amount of garbage collection. I'm going to try to implement some of it, but it's just guesswork as to whether or not it will actually improve things or not.

The recommended garbage collector for real-time applications is the copy collector. Or the treadmill collector, which is a variation of the copy collector.

All JVMs have the copy collector, but only for young objects. You can make it being used for all objects by increasing the young generation size. Set -Xmn<value> to the highest value the JVM allows, like "-Xmn511M -Xmx512M" and the copy collector will become the main GC. There are other parameters as well, but I can't find them right now.

Anyone know where to find the fast math pages/discussions? I did a search on "fast math" and couldn't find it. I know there was a number of places where alternate code for doing various trig functions was discussed, but I can't find them now.

I think it's User:Rednaxela/FastTrig.

(I didn't really notice any speed improvement when I tried adding it to Diamond, so I removed it, but maybe I screwed up or it would help other bots more.)

With Combat, profiling said all trigonometry added together were consuming about 8% CPU time. Dropped to less than 3% after fast math classes were added. Not too much of an improvement, but it was an improvement.

Thank you. I may give it a go. In the meantime, I've figured out how to make a few significant improvements that reduce the number of trig functions used. In particular, I have a method in my robot predictor that calculates sin and cos for every predicted robot movement; revising it to only recalculate sin and cos when heading changes actually made a significant improvement.

This version is like a fresh breath of chaos. Oh, the mischief I'm causing. :P

One of the few altruistic bots: beating the strong and helping the weak ;-) But the overall result is ok, closing in on Dookious. If you can find a way to rule out those 98->75 scores you'll be close to #3 and way ahead of me.

I've been impressed with the improvements in GresSuffurd. It has inspired me in my efforts to improve XanderCat.

Nice work!

Though it looks like you still have a few bugs in your specialized modes. A 26% score against GeomancyBS is almost as bad as 70% against Idem (a nano with Linear Targeting).

UPDATE: Version 12.2 is much better against those two bots.

Indeed. There is still some room for further improvement if I can iron out those trouble areas without hurting performance elsewhere. I'm pretty sure it can be done.

Really nice work, congrats! I wonder if you've got Skilgannon thinking that maybe Bullet Shielding is the gateway to 91 APS - I know I'm thinking it. :-)

Indeed, seeing the diff between GeomancyBS and DrussGT has had me wondering about this for quite a while...

Wow... Should I just hold my congrats until you get to #1? =) Friggin' awesome!

Thank you. I really didn't expect to see such success with adding a new mode to XanderCat. But after a lot of work unsuccessfully trying to improve the main surfing drive, I have to say I'm most pleased with it. :)

I'm also surprised how much it helped. I also think there might be scope for the opportunity to open up a shadow enough to completely hide in it with regular surfing, rather than just have a separate mode. And to get the full magnitude of the improvement, I think this diff is more relevant, and even more impressive. Never mind 91APS, 92 could be a possible target.

Speaking of bullet shadows, I think active shadowing could be possible in a 1000/1000 field. It could be used in Melee endgame duels to great effect. First, check if a full shadow is possible, if it is, then fire a low power bullet to create the shadow and move accordingly. If a full shadow is not possible, then just surf and fire normally.

One problem I could see with this is that you might not gain any energy by firing 0.1 bullets, because at long distances in endgame, 0.1 bullets are pretty regular. Also, with the enemy also shooting low power, you won't have much advantage in distance ratios to get a wide shadow because of bullet speed differences, but with enough distance that shouldn't matter that much I guess. Hmm. Food for thought...

I'm not sure that's true (edit: active shadowing more effective on larger fields). Being far away means the shadow has had time to grow, that's true. But another key to creating big shadows is for the bullet/wave intersection to happen as soon as possible after the enemy wave is fired. The farther away you are, the harder it will be to do that with any precision. It might be more effective to stay close and create accurate shadows than to gamble from far away.

Edit: Sorry, my argument kind of misses the point. With Robocode accel/decel, it's more about giving yourself enough time to get there. So I think you're right.

/me suddenly wonders if he's re-tuned his distancing since adding Bullet Shadows... =)

I don't understand, I thought as long as there was a 36px area within reach that could not possibly be hit, a bot in that area would be completely safe.

You're right, I was being dumb.

I wonder if this full shield idea is applied in teamrumble. 1200x1200 battlefield and 5 simultaneous shots which can be coordinated to create huge shadows.

It isn't, but it sounds promising. It could also work in twin duel: take out the weak enemy then have your two bots shadow/shield each other until the enemy leader runs out of energy.

This would be tough in Twin Duel with the code size restriction. But I think MegaBot 2v2 would be interesting at this point.

Not necessarily. Make both bots instances of the same class, give them Waylander's gun, fire 3's at the enemy grunt until it's 1v2, then have them hide in corners and shield each other every time the enemy fires. I believe Rednaxela was planning to try something like this. Let's beat him to it! :)

In team battles, there is the added complexity (and fun) of needing near perfect team radar locks to make bullet shield viable.

Provocative movement, which owns bullet shield, is also a greater threat in team battles than it is in 1v1.

Neuromancer does 'bot-shadows', where if a wave passes over an enemy, the section that the enemy covered is marked as safe. Unfortunately it didn't help in terms of score, I suspect that my wave locations and firetimes aren't accurate enough. Also, if the wave was fired far enough away that it passed over another bot it probably wasn't weighted very highly anyway.

In the old days of robocoding, 'bot-shadows' were called bomb sheltering.

Ah, I knew I didn't come up with that idea independently...

One bad thing is that there is a lot more volatility in the score, based on whether or not the "scenario" (my terminology) correctly identifies which opponents to activate against. And the score difference can be huge. While in the past the Rumble score was pretty stable after about 2000 battles, I would say it now takes more like 6000 to 8000 battles. Watching the progression on version 12.2, after about 1200 or so battles the APS was around 86.85 (excitingly close to WaveSerpent), but now with 3800 battles it has dropped to 86.50, in danger of dropping below Dookious after such a solid first showing. I still don't think we can say whether or not it will level out above or below Dookious.

It should also be noted that the effectiveness of bullet shielding will be less if implemented by top surfers. The score boosts will be less against the vulnerable, and there is more opportunity to lose performance against the non-vulnerable.

I also have to take an extra step in future development. Due to the volatility, whenever I am working on anything but bullet shielding, I have to disable the bullet shielding mode to get more reliable testing results.

I know I am kind of focusing on the downsides here, but I think the upside is pretty obvious, thus I am commenting on the more subtle issues this new mode introduces.

After looking at the individual battles against a number of opponents, I might add that with more testing and time, I can probably improve the scenario to activate a little more reliably against vulnerable opponents. This could eliminate at least some of the volatility and possibly give another 0.2 to 0.3 APS boost (assuming 12.2 holds around 86.5). I will have to create a challenge of shielding vulnerable opponents and collect more diagnostics to see which of my conditions for continuing bullet shielding are occasionally violated and why; then I can hopefully tweak it a bit to make detection more reliable.

I've actually been thinking about this. It's not possible to know how well you can dodge their targeting without risking poisoning their targeting into shooting non-GF0 bullets, which makes it difficult to know against bots that you can dodge perfectly if they are secretly a Hydra, and the 97% is a 30% increase in possible score, or if they are just DoctorBob and the 97% is a 3% decrease in score.

That's true, but bullet defenses are very unexplored areas of Robocode strategy. Some sort of "Adaptive Bullet Shielding" could be very successful.

0.35 is a lot after 2k, and I would certainly buy that this increases your volatility, but I'd wait and see if future versions behave similarly to say for sure. I've seen lots of variance above 2k battles with lots of bots, not just after adding sensitive multi-mode stuff. It was something I never really noticed until we had a surge in RoboRumble power from KID's clients and bots started actually getting to 5k-8k battles. It could have also been something like a few enemy bots crashing/skipping turns on one client and then behaving normally on another client that started up later.

I tried the top bots that came to mind and at first couldn't find any over 55k. But apparently you didn't quite set the record (but close). Only kept highest for each bot.

java -jar robocode_1.07/codesize.jar ./robocode.rumble_1730/robots/*.jar
...
45842	293199	74	kid.DeltaSquad.DeltaSquad_.1.jar
46143	289610	77	kid.Gladiator_.7.2.jar
47086	335204	209	Homer.Barney_1.0.jar
47269	458846	232	pedersen.Grishnakh_1.0.jar
50883	406124	107	voidious.Diamond_1.7.24.jar
52283	147604	107	positive.Portia_1.26c.jar
54049	307732	92	florent.XSeries.X2_0.17.jar
54352	249048	48	aw.Gilgalad_1.99.5c.jar
55297	509698	190	xander.cat.XanderCat_12.0.1.jar
58387	139768	35	jk.mega.DrussGT_2.8.8.jar
227423	474856	62	Krabb.sliNk.Garm_0.9y.jar

Not sure what the heck is going on with Garm! :-) Maybe he includes some big external library? I know I've thought about pulling in Guava, though there are ways to only pull in what you use.

Does he have the largest class size though? Speaking of codesize, I tried my hand at rewriting the tool.

Seems like it, but I don't really know what that means. And it (thankfully) sorts by code size, so it's non-trivial to check for greatest class size. Is that just your code + all classes you reference (including the Robocode and Java libraries)? Anyone know?

I think I have to throw in some extra bytes to the 12397 I use now, and pass Phoenix before you do. But hey, I am no Java programmer, for the current assignment I use OOPerl . . .

I am actually trying to aim towards the other end of the field lately.

I don't particularly care one way or another, but I also don't take pride in my large code size. It's worth noting that a laughable ~10k of Diamond's code size is the perceptual gun he uses for the first few shots. :-) Factoring that in, I don't think Diamond's too bloated for a full-featured 1v1 and Melee bot.

Heh, after Garm DrussGT comes in second =) I suspect it's because I have several Precise Prediction methods, my pixel-perfect Waves/Precise Intersection ones for first wave calculations and my high-speed ones for second wave calculations, and then I still have all of the code for the precise predictions which tracked when an enemy would fire and what segments they would see even though they aren't being called. Also, all of those buffers that are hard coded add ~17000 codesize.

I was looking into building an AS gun using spectral clustering, and when I packaged it, it pulled in the entire Colt library, which brought DrussGT to 217374 codesize bytes and 760KB of jar file! Needless to say, I started looking at other matrix libraries...

Anyone have a huge challenge file they can post or send to me that includes a large number of rumble participants (anywhere from 100 to 1000)? My biggest one contains 58 opponents and it still sucks bigtime. Okay, call me lazy, but I haven't been willing to hand type out a challenge with hundreds of participants, and I can't access the query API and therefore can't use the test bed maker. I feel like I am abusing the Rumble a bit because of these things, releasing more versions of my robot than I should. Before I go spend an hour or more hand typing out a challenge or writing my own tool to build challenge files, I'm hoping maybe someone just has one they can provide for me? Or maybe someone with Query API access can run a test bed for me?

I'd be happy to run BedMaker for you when I'm home this evening (and/or post some of my bigger .rrc files). Until then, you could try the ones I ran for User:Tkiesel a while back. [1] [2] ("150 bots that deBroglie rev0108 scores 57-95 against")

I'm sure some bots are not currently in the rumble, so it's possible you'll be missing some.

I could get by on those, but if you run me one, I'm thinking maybe 250 bots that XanderCat scores 0-95 against.

Nice timing, just got home. Here's 3 versions of that config: [1] [2] [3]

Muahaha. It will take forever to run, but by goodness, maybe it will actually be useful this time! Thank you!! Now if I can just figure out how to justify upgrading my old Core 2 Duo with a top of the line i7 processor...

PR: i can share with you my i7 with help of Distributed robocode :)

Wow. That looks a lot more advanced than I thought it was. I've actually got a Linux server in my basement that I use as my network storage (no media streaming, just backup) and code repository, but otherwise it does nothing. It's not that powerful, but since it mostly sits idle, I will look into setting it up as a node for distributed Robocode.

Ok, say if you setup it - then i configure portforwarding, so you will available connect to my DR servers

I'm still planning on doing this, but it might be a couple more weeks. When I went to my basement to set up the server, I noticed the CPU fan had stopped working. This is not really a problem with the CPU mostly idling as it does now, but if I set up Distributed Robocode on it, I need a working fan. I'll be putting a new fan on it sometime in the next couple of weeks, after which I can get Distributed Robocode going on it.

No problem, and good luck! As for the Robocode-inspired upgrade, I gave up on justifying it and just did it last summer. :-)

I start write Robocode Development Kit this month and currently it's just can download all participitians and genereate challenge files. There're no public available builds, but you can see project here: [1]. If you interesting, i can upload build of it for you.

I'll take a look at it. Thanks.

Frequent resubmits to the rumble were annoying me in the past.

But now I use a custom priority battles algorithm which places my bots above others until they have about 15 battles per pairings. :P

After that it resumes normal priority battles driven by the server.

Nice. I've always thought you should have control over what battles your client is willing to run. After all, it's not like you are contributing any less battles for other bots than someone who isn't running a client at all.

So I've put a lot of time in on XanderCat, what is supposed to be version 12. Wow is it hard to improve at this point. I had roughly 10 new ideas for improving and all of them had no positive effect. The only improvement so far has been in some refactoring I have done, which has simplified a few things, but not changed my score any. There are a few instances of robots that some other top competitors score nearly perfect against, but XanderCat only gets maybe 85 or 90. I think I may figure out how to make some improvements by closely scrutinizing those instances. I also want to take a closer look at each of my special case scenarios to ensure none of them are counter productive -- this includes my ram escape scenario, anti-mirror scenario, my targeting detector drive engagement scenario, and my bullet shielding protected gun wrapper. I don't expect to find anything wrong though; probably will just be verifying that they work as intended. Ho hum...what next...it seems like there should be some big gains to be had, given XanderCat is still 5 APS behind the leader, but darned if I can find them. I'll keep trying when I have the time though...

How you account distance danger? I get with Tomcat 0.7 APS when take in account min danger to enemy on path instead of distance on last point. Looks like it can help you.

I took a look at some comparisons vs DrussGT and Diamond. One thing that strikes me is that in a bunch of them, your survival is much lower, and DrussGT/Diamond get very high survival. This could just be a side effect of not being strong vs those bots, but it could also be a deficiency in your bullet power selection. Actually one of my most recent improvements to Diamond was in my bullet power formula.

It definitely seems like there are a bunch of matchups that should give some clues. And I agree distancing is important. I gained a bunch just from changing the shape of the curve I use when factoring distance into my danger calculation.

Thank you for the ideas. I'll definitely have another look at each of those things.

Alright -- nobody run any more Rumble battles, because I currently have the top score against DrussGT. :-P I still don't know what I did that made me lose about 0.3 APS, but even if I figure it out, I won't be putting up a new version for a little while. I have some new changes I'm working on and I intend to run a gazillion test battles before the next time I release anything, so for awhile I'll be out of the top 10.

I just compared the versions 11.15 and 11.11 with eachother, and the new version is much worse against 'simple' bots. 97% dropping to 88% is very very bad, the other quadrupled its score! My tip: run battles against f.e. Supersample.SuperCorners and watch what is happening. Loosing rounds against such an opponent should not happen and the reason is hopefully easy to see (radarlock, gundirection).

GrubbmGait -- good advice. Definitey something wrong there. There is also one battle against Timbot where XanderCat only scored a 7 APS. I would guess an error slipped through where an exception can occur causing XanderCat to freeze and lose a round occasionally (or in the case of the Timbot instance, get shutdown completely). Whatever it is, it definitely needs to be fixed.

I'm pretty certain I found and fixed the primary bug (there may be an underlying secondary bug to fix, but the exception should be gone). I know I said I wasn't going to release another version pre version 12, but since it is an exception that is occurring, I feel I should go ahead and fix it. So I'm putting out 11.16 today.

Yes, yes, just one more version... =) I know how that goes.

I know some of you have said things about this recently, and version 11.7 of XanderCat is a good example. To be at the top, you really have to iron out all the little things to ensure there are not any score losses, even if they seem tiny. Squeeze every last point out of every feature you implement.

In version 11.7 of XanderCat, I updated my anti-mirror components. The new version was better in a lot of ways, and able to detect a wider array of mirroring tricks. However, my overall APS dropped enough to take me from 8th down to 12th place. The reason? More false positives on mirror detection (robots briefly detected as mirroring when it was really just coincidence). My new components, able to detect mirroring in a variety of new ways, amplified the losses from the false positives. Furthermore, it means I might be able to improve further than I had previously if I can completely stamp out of those false positives in a future version.

What other ways could I eliminate minor losses and see noticeable improvements in overall APS? What other ways might you be able to do the same? My next step will be to verify whether or not I'm losing any ground do to wall collisions (based on my latest stats, XanderCat runs into walls on average 43 times on every battle! But ultimately it depends on how much energy I'm losing from it, which I should know in another day).

That's pretty interesting. You can definitely squeeze some points out of rammers, but for me that was more a matter of personal pride / fun than really going for APS. My bullet power, distancing, and kernel density formulas had a surprising amount of room for improvement even after Diamond was at #2

On wall hits, I'm curious how that turns out for you. Dookious and Diamond hit walls sometimes in 1v1. In Melee, Diamond uses precise prediction to never choose a movement option that would hit the wall in the next few ticks, and the result is a really smooth and nice and beautiful Melee wall smoothing that never hits walls but really hugs them. I tried applying this same logic to my 1v1 movement to avoid all wall hits, and it was a super huge pain to get it all working right, and then I gained no points from it. So I removed all the mess and just left it at hitting some walls sometimes.

As a thought on avoiding false positives in mirror detection, I'm pretty happy with the margin of error calculation setup I use in my flattener enablement. Basically, the hit % threshold I use to enable the flattener has a margin of error added to it, so I only enable it if I have 95% confidence that the enemy's "true" hit % is over that threshold. Maybe you could do similar and be really conservative in your detection early on and gradually get more aggressive as the battle goes on and you gather more data. I'm not sure how that will hold up with multi-mode bots but it's definitely something I'd explore.

Sorry to double post, but on the wall hits, consider: if you can slam into the wall at full speed and take a bunch of wall hit damage and it's your best chance to avoid a HOT or linear targeting shot, do you really want to rule it out as an option? Hitting the wall doesn't cost you score unless you lose a round, but taking some bullet damage costs you a lot of % score against such a bot.

Not enough battles to know for sure yet, but looks to me like eliminating hitting the wall will only have a very marginal impact on score. Version 11.8 is currently showing only 0.05 APS better than 11.7.2, despite hitting walls only about 1/3 as often. Oh well. On to the next possible improvement. I'd really like to get a solid lead over Hydra.

Here's an outlier: [1]

XanderCat 11.7.1 scores 57.46 against Druss GT 2.8.5 -- Ironically, ran by Skilgannon so no crying foul play. ;-) Due to a run of luck on the Rumble, PL score for 11.7.1 after 1 battle against each opponent was silly high -- only losses were to Diamond, Tomcat, and WaveSerpent. Maybe I can pull it before it starts dropping. ;-) The change in 11.7.1 looks like it didn't fix what I broke in 11.7. 11.7.2 coming soon...

What's the secret to beating this guy? The better my robot gets, the lower my score against PrairieWolf gets (current PBI is -25 and getting worse). I'm at a point now where I have a lot of trouble getting over 50 APS against PraireWolf, despite there being a lot of much lower ranked robots that seem to have no trouble at all. I've been mucking around with different aspects of my robot to no avail. I guess I have my new nemesis.

PW has a bunch of movement modes so maybe just one of them is giving you trouble? Have you watched some battles?

A long time ago a version of Dookious was losing to PrairieWolf. I eventually watched enough to find that PW's "vibrating" movement mode was confusing the heck out of me - I kept trying to shoot ahead of him, not shooting because my gun wasn't turned all the way, then he'd reverse direction and I'd start aiming the other way. So I'd just never shoot.

PrairieWolf's page says it has Circular Targeting, but it looks like it must have some sort of averaging or randomization. I would suggest using something like active bullet shadowing or rsim style Bullet Shielding, but I realize that's easier said than done.

I would guess it's PrairieWolfs Reflection movement (Mirror Movement), but XanderCat is doing well against other mirror bots.

I'm pretty sure it is not PrairieWolf's mirror movement. I paid special attention to that, and from what I could tell, XanderCat handled that movement without any trouble. From what I can tell so far, it may be a combination of subtle problems. Ultimately my hit ratio is worrisome low, and PrairieWolf's hit ratio against me is worrisome high. The multiple drive modes may be polluting my gun data some, though I have data roll that should help with that. As for why the hit ratio against me is high, that's more of a mystery. PrairieWolf looks to have one of the highest hit ratios against me in the entire rumble (excluding close range fighters, though I am not looking very close at bullet power either).

There are other top bots that have trouble with PrairieWolf, so I'm not alone there (I think FireBird is at about 50 APS against it). But some other top robots beat PrairieWolf by quite a margin, so I know there is lots of room for improvement. For now, I remain a bit stumped. I have a few other changes to push forward with for now, but I will definitely be back to focus on PrairieWolf in the future.

Hey, congrats on 8th place! I think every spot you move up at this point is like another milestone. :-) (I guess 1/2 and 3/4 are pretty close though.) And a belated welcome back...

Thank you! The next rank up is a pretty big step. It will take some magic, but I'll work on it. I was thinking my next step, partially out of curiosity, will be to do a new round in the gun and drive challenges. I'm interested in trying to determine if there is more room for improvement in my guns or my drives, or if they seem pretty balanced at the moment. I've been playing with a lot of parameters, but I think continued effort on improving my segmentation (in both the drives and guns) offers the most promise for further improvement. I don't think I will be beating Diamond any time soon, but with a little more work, perhaps I can at least give you a moment of pause anytime XanderCat comes up in the challenge queue against Diamond. :) - Skotty

Just noticed, that XanderCut get very close to the group challenging the crown:) And congrats with scores against Shadow - i think tens of robocoders dreaming about it:)

Not sure what I broke in version 11.1. I'll be looking into it over the next few days. The changes I made were mostly cleaning up the code, so performance shouldn't have changed much.

Just to close off this thread, the problem with version 11.1 was with the wave cache I created for the 11.x series of robots. Wave creation is far more expensive with Precise MEA turned on in my Xander framework. At the same time, each guess factor gun would create a wave for itself when aiming before any actual wave yet existed. This resulted in pretty much the same wave getting created multiple times, resulting in the same Precise MEA getting predicted multipled times, which was absurdly expensive. The wave cache was meant to help fix the problem by allowing waves to be reused. However, the implementation was a bit flawed, and waves that were not quite identical could get reused. Ultimately, I really only needed to cache the Precise MEAs, so I changed it to an MEA cache instead of a wave cache, which actually simplifies it, and more importantly, fixes the problem.

One last note -- my Precise MEA calculation could be improved, and I may work on it in the future. The current implementation works, but it uses a shotgun brute force approach which I should be able to improve if I put my mind to it. What makes it so tricky is handling situations where the robot is near walls in combination with taking drive limitations into account (turn rate, accel/decel rates).

Not sure how many people follow it, but FYI, I hope you're not too bothered by XanderCat being ommitted from the @roborumble Twitter feed for now. I'm pretty sure it's interpreting your bot name as a URL and failing somehow (they started auto-shortening all URLs recently). Even trying to post the same tweets through the web interface errors out for me. I reported the issue a week or two back but no response yet...

Although I'm not quite sure what you are talking about, as I don't use Twitter, I just tonight fixed the Download link for the JAR on the XanderCat wiki page. It has been broken for a number of weeks (I didn't realize it until today). I just mention it in case that might have had something to do with it. Or maybe that's completely unrelated. I don't know.

You know, I'm not sure how it works, but I use the NoScript add-on in Firefox and it currently blocks code from the Twitter domain twimg.com. Maybe that was causing the issue? I could unblock it.

I'm a couple days late on this, but wow, congrats on entering the top 10! Ascendant was #1 when I started Robocoding, so he is a particularly special milestone for me. =) And you're also beating Shadow over 11 battles. Truly awesome.

Thank you! I've put a lot of time into it, and it's been a lot of fun. Are there any other high ranked multi-mode robots? I got the impression that there are not too many. I'm happy to add another. :-)

Congrats indeed! Y'know, at one point RougeDC was bordering at the top-10, but these days the top-10 is much trickier!

About multi-mode robots, what about flatteners? I think all top-10 robots with the lone exception of Scarlet use a flattener, and personally I consider a flattener to be a form of multi-mode technically :P

Tomcat also has no flattener, which can be treated as multi-mode. Tomcat is more continously adapt his behavior to dodging or flattening, than turn on or turn of flattener by some events

I think arguing that a flattener is multi-mode functionality is reasonable, though I think the majority of people would not consider it so. On the flip side, someone could argue that my robot is only marginally multi-mode, despite it various "scenarios", as about 95% of the time it is using the default gun array, and about probably 70% of the time it is using the default wave surfing drive.

Congrats from me to, although I am not so happy that my (flattenerless) robot now is pushed out of the top-10 again. But I must warn you, this triggers the drive to do some real changes instead of tweaking. ;-)

Truly great to see hard work result in a top-10!

Congrats from me too:)
Looks like i must start worry not only how to get crown, but also how to save third place:)
And are you remember, what i speak about our own TOP-3?:) Chase?:)

Wow, that was worth doing! Finally fixed my flawed bullet shadows implementation I put in place back in version 9. My rank didn't change due to being in a bit of an empty area in the APS distribution, but APS increased by about 0.4, and for the moment, I'm tied for 4th in PL. However, I have a lot of battles that are very close to 50 in % Score; I could see possibly losing my lead against CassiusClay, Shadow, and Scarlet (wee! I've never beat Scarlet before!). Meanwhile, Diamond, Druss, and Tomcat continue to be quite deadly; I'd be happy to make 45% against any of them.

Looks like yours BS implementation still broken or not prefect, because BS must give you about 1 APS (at least DrussGT's, Diamond's and Tomcat's results say so)

Not necessarily. My original buggy implementation was sort of working; it just wasn't working very well. To tell how much difference my new implementation makes (versus not using it), I would need to run a new test with it turned off.

On the heels of Tomcat, with XanderCat I achieved a decent rank, then figured out how to fight the top robots but it dropped my score, then I got my score back up to where it was. I'm happy with top robot performance for now; top 20 in PL score (PL score was 1630 earlier today, but it can fluctuate, as a lot of close calls), only a couple of robots that can make over 60% against XanderCat now, and I finally figured out how to hit Chase's robots (or at least, not miss them as badly) -- and it's not tweaked yet, so watch out Chase! *grin*

However, now I'm a bit stuck. I'm underperforming against some easier and mid pack robots like csm.NthGeneration, janm.Jammy, mcb.Audace, and morbid.MorbidPriest, to name a few. Not always the same problem. In some cases, I should be dodging bullets better; in others, I should be hitting with my gun better (my gun is not handling oscillating movements as well as it should). I've been monkeying with my data system for quite a few days now without making any headway. My top idea involved mixing and matching segmenters, selecting by which segmenter combination is providing the best hit ratio (either high or low, depending on if gun or drive). I still think it's a good idea, but I haven't seen positive results with it yet. Might be time for another break.

Le Gasp! You hitting my robots, nuuuuu, my poor cute little robots! T_T

In other news, I find that most of your gains come from bug fixes and structural changes. Segmentation is mostly there for fine tuning and for base targeting ability. You should focus more on examining how your targeting works and thinking up ways to improve that. Not saying to ignore segmentation, but it shouldn't be your primary focus until you are already doing pretty well with it's targeting.

I've planned out my next steps now. First, I'm going to do a little refactoring of my code to seperate responsibilities a little more for my guess factor / wave surfing code. I'm going to seperate the basic parts out into a hit logger (the component that logs hit and visit data in a tree), hit log reader (the component that decides on what data to select from the tree), and factor array constructor (the component that decides how to use the data). This will make further work easier and allow for improved efficiency in some areas (will be easier to cache certain data). Then I'm going to more closely review my tree code for issues and try a few variations on my selection algorithm that I think could yield good results.

I've got a real KD-Tree running now for my data. It runs faster than my old setup and should be more precise, but to my disappointment, it hasn't really seemed to improve things much. In fact, I've gotten worse at robots that do things like simple linear targeting. Overall APS is down. On the flip side, my data rolling, flattening, and other top robot fighting tricks are working pretty well, my PL score is the highest it has ever been, and only 3 robots in the entire rumble are scoring over 55 against me last I checked. I've carefully scrutinized, and my top robot tricks are not kicking in against easier robots and messing things up on them...something else is amiss...I just can't figure out what.

Robocode results are not always logical. Looks like you have to check against xiongan or timmit.nano.Timdog what is going on.

Good news! I found one, possibly two bugs to fix. One bug has been around for a long time, the other I think just introduced when I switched to a true KD Tree. I'll post the details in my next version notes.

I just barely squeaked into the top 20. I'm happy with that for now. I'm clueless on what to do next, so it will be awhile before there is any chance of further movement. I need to do some thorough bug searching in my data system, and/or do a large number of parameter tweaking tests against a large test bed, and/or get some brilliant new ideas. I don't see any of that happening soon. I'll probably focus on examining performance against mcb.Audace 1.3 first. Audace has a mostly oscillating movement. Most top 20 robots score 90 to 95 against Audace, but I only score 82.

Do you have any 'time-since' attributes in your gun? Like time-since-deceleration or time-since-direction-change? I found they work wonders at hitting oscillators...

Congrats! Well, even if you never work on it again, XanderCat is certainly a bot to be proud of. That said, it seems like I've heard you say that several times before and then you just kept climbing up the ranks... =)

I'm currently playing around with a time-since-direction-change segmentation in the gun, but it's not showing much improvement. I need to examine the data manually to see if there are any bugs. So the next things I'm going to do is to write some new debugging code that prints more information to screen, as well as write debug data to disk, so I can do a thorough analysis. I was also debating writing a painting framework for debugging, that makes it easy to turn debug options on and off and move informational printouts around on the screen. This will take some time to complete. In the meantime, I may set up 10.7.1 to run in the gun and drive challenges. I also want to explore updating RoboResearch to run battles in a different manner -- something I think JDev or someone has messed with before -- to try and reduce the startup times for battles. Currently, when I run RoboResearch, about 9/10 of the time it takes is waiting for the battles to start, with only about 1/10 of the time going to actually running the battles. Which is rediculous.

A thought about the time-since-direction-change - I'm not sure how your gun works, whether you're using discrete bins or raw value kNN type of gun, but remember that the likelyhood of a TSDC being a very high value is quite low, whereas it is more likely to be a low number. Both Rednaxela and I use something like 1/(1 + k*TSDC) in our kNN guns to nicely smooth it out so that all values receive a similar likelyhood of occuring.

I've learned a lot recently about what works and what doesn't against specific robots. I could make one heck of a little black book robot if I wanted to. Unfortunately, I am finding it to be very difficult to build a generic classification system, as I am not finding any easily detectable common threads/attributes on which I can rely upon.

Looking at all the hit ratios, I think my gun system is pretty decent (though always room for improvement). It's my drive system that needs more work. I've considered trying to go back to a VCS drive system. Also, I continue to consider trying some more range-search variations of my DC drive system, which I think holds some promise. I think I'll start playing around with the latter.

For Version 11, I'm experimenting with going back to more of a VCS drive rather than the KNN based drive.

One quick little note about VCS drive versus KNN drive: In my experience, when one implements both, the biggest differences in performance are typically implementation details (how the danger is formulated), rather than anything inherent to VCS or KNN. If one isn't very very careful about how one is formulating the danger as to make it equivalent, then one won't have a fair comparison between the VCS and KNN systems.

I'm not really looking for a fair comparison. Just looking to find out which one I find to be easier to work with. To me, each has it it's own unique advantages and disadvantages. But last time I had both, I was doing better with VCS. Time to see if that still holds.

And I'm not really sure if I really understand what you all define as VCS. To me, when I say VCS, I mean I have a ton of factor arrays, and each data point results in a factor array being updated. At surf time, I pick one of the factor arrays to surf. This being opposed to my version of KNN, where the data is saved in the KD Tree, and at surf time a single factor array is constructed after points are pulled from the tree.

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.