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| What is the rank of Gnu go 3.8 (bot) http://www.lifein19x19.com/viewtopic.php?f=18&t=10681 |
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| Author: | Darsey [ Sun Aug 10, 2014 6:38 pm ] |
| Post subject: | What is the rank of Gnu go 3.8 (bot) |
Good morning I have in quarry (one program to play go for computer, I got it in my laptop with linux mint). I usually use it to practice some josekis, answers or see games. But I am man of risk and I want try be more strong that my computer, but the bot of quarry, that is GNU GO 3.8, always humbles me. Therefore, I want know what rank is my archenemy GNU GO 3.8 when I want play without internet. |
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| Author: | pleiade67 [ Sun Aug 10, 2014 11:11 pm ] |
| Post subject: | Re: What is the rank of Gnu go 3.8 (bot) |
The last stable versions of gnugo 3.7 and 3.8 (non Monte Carlo) are usually rated in the range 6k-5k KGS at their best. Source: http://senseis.xmp.net/?GNUGo http://senseis.xmp.net/?KGSBotRatings http://senseis.xmp.net/?KGSBots |
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| Author: | Darsey [ Mon Aug 11, 2014 4:49 am ] |
| Post subject: | Re: What is the rank of Gnu go 3.8 (bot) |
Very thanks. I didn't know these articles in sensei. |
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| Author: | Mike Novack [ Mon Aug 11, 2014 5:56 am ] |
| Post subject: | Re: What is the rank of Gnu go 3.8 (bot) |
If you need* to use the computer as an opponent don't try to play even games against a much stronger program or set the program's playing level weak enough to be able to play even games against it. You either will never see when you do things right vs doi them wrong (in the first case) or will learn the bad habits of that particular program/setting in the second. Gnugo is using an AI (go knowledge based)based evaluator. It will play too passively, but OK for you to use until you learn how to beat it. Figure max strength at about 5-6 kyu (all a good AI go knowledge based evaluator can do). Fuego uses a MCTS evaluator and will not play passively. When you get down to playing even games (or even 2 stone games) against gnugo switch to fuego. Meanwhile, begin taking handicap stones. First discover how many you need to win perhaps half of the games and then take a stone less. Ideally you want to lose 3-4 games for every one you win to maximize learning. The times you do win you presumably didn't make the same mistakes as you did in the games you lost and you might come to recognize the traps you avoided. *for example I can't afford to tie up our phone line to play against a human on a go server, too long at a stretch. |
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