Re: [computer-go] Life and Death
I think that the original description of the position should have said 'killable' rather than 'dead', and that David missed the fact that it is White to move. At 08:06 27/03/2008, Hideki wrote: David Fotland: [EMAIL PROTECTED]: I just looked at this position and it looks like a win for black in the first position. Many Faces evaluates it as a win for black, and plays c1 to save the lower left black group with almost no thinking time. Mogo is correct because the lower left black group is not dead. I'm sorry if wrong but the black seems dead by B5 after C1, isn't it? -Hideki David -Original Message- From: [EMAIL PROTECTED] [mailto:computer-go- [EMAIL PROTECTED] On Behalf Of Matthew Woodcraft Sent: Sunday, March 09, 2008 11:49 AM To: computer-go Subject: [computer-go] Life and Death I've included two 13x13 positions below. In both positions it is Black's move. The first position is simplified from a real game. Black has two enclosed dead groups, and White has a small but easy win. The second position is a modified version of the first in which the dead groups are more obviously dead. If I try MogoRelease3 playing as Black on position 2, it shows a 20% score and resigns either immediately or after a couple of moves. If I try it on position 1, it shows a score of 70%+ for Black, and continues to play until White takes steps to remove the dead groups from the board. I've tested with up to 2^24 playouts. I have tried increasing --collectorLimitTreeSize and --limitTreeSize (like bigMogo in the scalability study), but I can't set them much higher than the default on this machine without running out of memory. I'd be interested to see if someone with a bigger computer can find out what resources it needs to judge this position well, and to see how other engines do. Position 1 (;GM[1]FF[4] CA[UTF-8] SZ[13] HA[0] KM[0.5] AB[jb:kb][cb:cc][kc][bd:cd][jd:ld][be][he][cf:df][bg][gb:gh] [ig:ih][jh:kh][eg:ei][li][aj:bj][hi:hj][lk][al][ck:cl][ji:jl] [dm][im] AW[ia:ja][ib][hc:jc][db:dd][hd:id][ce:de][ie][ke:le][bf][hf:jf] [lf][jg:kg][mg][lh][ai:di][gi][mi][cj][ej:gj][ij][dk:fk][hk:ik] [dl][il][bm][fl:fm][hm] ) Position 2 (;GM[1]FF[4] CA[UTF-8] SZ[13] HA[0] KM[0.5] AB[jb:mb][cb:cc][kc][bd:cd][jd:md][be][he][cf:df][bg][gb:gh] [ig:ih][jh:kh][eg:ei][li][aj:bj][hi:hj][bk:ck][lk][al][cl] [ji:jl][im] AW[ia:la][ib][hc:jc][mc][db:dd][hd:id][ce:de][ie][ke:le][bf] [hf:jf][lf][jg:kg][mg][lh][ai:di][gi][mi][cj][ej:gj][ij][dk:fk] [hk:ik][dl][fl][il][bm:cm][em:fm][hm] ) -M- ___ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/ ___ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/ -- [EMAIL PROTECTED] (Kato) ___ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/ -- This email has been verified as Virus free. Virus Protection and more available at http://www.plus.net ___ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
Re: [computer-go] New scalability study progress report
This is very cool. As of 261 games played, I find it very difficult to guess whether the mogo curve is beginning to dramatically flatten, or will continue to rise steeply. I have a few questions. I can't see the cross table, I guess you haven't put it up yet? How do you decide the pairings? I think more even pairings would be more informative, and lead to faster convergence, but I suppose you don't just want bots playing adjacent bots? Are you going to ask for volunteers to run this script? I think it is more likely to give interesting results than the SETI thing. At 01:31 19/01/2008, Don wrote: The new scalability study is in progress. It will be very slow going, only a few games a day can be played but we are trying to get more computers utilized. I will update the data a few times a day for all to see. This includes a crosstable and ratings graphs. The games will be made available for anyone who wants them. Although it's not on the graph itself, Gnugo-3.7.11 level 10 is set to be 1800.0 ELO.The bayeselo program is used to calculate ratings. Results can be found here: http://cgos.boardspace.net/study/index.html bayeslo assumes all players are equal strength until significant evidence proves otherwise, and with only a few games played each, the graph will look truly strange, but this will correct itself in time. Each data point in the x axis represent a doubling in power. There are 13 doublings represented and mogo was set to approximate FatMan in strength at each point - at least at the lower level but only with a very crude and rough estimate. Since I don't know how well mogo scales compared to FatMan, we can only wait and see how this works out at the top levels. - Don ___ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/ -- This email has been verified as Virus free. Virus Protection and more available at http://www.plus.net ___ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
Re: [computer-go] U. of Alberta bots vs. the Poker pros
At 02:58 28/07/2007, Arend wrote: On 7/26/07, chrilly mailto:[EMAIL PROTECTED][EMAIL PROTECTED] wrote: This is a remarkable result. I think poker is more difficult than Go and of course chess. I am as surprised by this statement as everyone else. Of course you have to develop some mixed strategies, try go guess implied pot odds, folding equity etc. but assuming you have access to a large database of high level poker games to analyze, why should it be that hard, esp. in 2-person limit Hold'em? Arend It seems plausible to me that poker should, in some sense, be more complicated than go. I'll ignore the massive savings from clever search tricks in both games. In order to get optimal play in go, it is necessary to search over all legal positions, of which there are fewer than 3^(19^2). In order to get optimal play (ie a Nash equilibrium) in poker, it is necessary to search over all strategies (of both players). A strategy is a map from your knowledge (the cards you can see and the opponent's bids) to an action. Even if we assume a single round of bidding, the number of strategies for a single player is roughly (no. of actions)^(no.hands * no opponents bids). This is massively higher than the number of go positions. ___ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
[computer-go] OT U. of Alberta bots vs. the Poker pros
At 18:20 26/07/2007, Jeff Nowakowski wrote: On Thu, 2007-07-26 at 18:14 +0200, chrilly wrote: Chess/Go... can be played in an autistic way. There is no need for an opponent model. Ah, an opponent model. Where's the poision? http://www.imdb.com/title/tt0093779/quotes#qt0250635 Too much rock, paper, scissors in poker for my tastes. Can there ever be a best player? At least in Go the differences in strength are very clear, and some guy off the street who learned the game a year ago is not going to win a tournament. -Jeff Rock,paper,scissors, also known as Roshambo is not an interesting game in my opinion (except perhaps for the human psychology involved). Because of the symmetry between the strategies, it is clear that objectively speaking, all strategies are equally good, even for multi-round games. There is a single optimal mixed strategy which chooses a move at random from a distribution for which each play has probability 1/3. However, if you break that symmetry, say by adding a 1/100 of a round bonus every time a player chooses rock, the game becomes more interesting, though it is still possible to find the optimal mixed strategy in a few lines of calculation. Any variety of poker is sufficiently complicated that it is very difficult to find an optimal mixed strategy, and therefore it is, as far as my interest in it is concerned, very different from Roshambo. Perhaps it is true though that even with an optimal strategy, the 'noise' on ones winnings would be so high that one wouldn't stand out from the crowd. ___ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
Re: [computer-go] U. of Alberta bots vs. the Poker pros
At 12:42 28/07/2007, you wrote: At 02:58 28/07/2007, Arend wrote: On 7/26/07, chrilly mailto:[EMAIL PROTECTED][EMAIL PROTECTED] wrote: This is a remarkable result. I think poker is more difficult than Go and of course chess. I am as surprised by this statement as everyone else. Of course you have to develop some mixed strategies, try go guess implied pot odds, folding equity etc. but assuming you have access to a large database of high level poker games to analyze, why should it be that hard, esp. in 2-person limit Hold'em? Arend It seems plausible to me that poker should, in some sense, be more complicated than go. I'll ignore the massive savings from clever search tricks in both games. In order to get optimal play in go, it is necessary to search over all legal positions, of which there are fewer than 3^(19^2). In order to get optimal play (ie a Nash equilibrium) in poker, it is necessary to search over all strategies (of both players). A strategy is a map from your knowledge (the cards you can see and the opponent's bids) to an action. Even if we assume a single round of bidding, the number of strategies for a single player is roughly (no. of actions)^(no.hands * no opponents bids). This is massively higher than the number of go positions. ___ Sorry, this isn't what I meant to say. A sensible strategy in poker has to involve bluffing, so it is a map from knowledge into distributions over actions. The point about it's being bigger than the space for go is right though. ___ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
Re: [computer-go] 9x9 games wanted and the next big challenge
Yes. This number is strongly dependent on strength and board size I think. Very roughly speaking, you can argue as follows 1) in a 9x9 game, the weaker player has only 1/4 as many moves in which to throw away the handicap advantage (compared to 19x19). 2) weak players lose so many points compared to perfect play that the final score (the difference between the number of points the two players lose) has a large variance compared to the value of a handicap stone. According to some early experiments I have made on a database of games played by humans on KGS, I'd say it is more likely to be 70 or 80 Elo points. Also, it is likely to depend on strength. I'll be able to give more precise data in a few weeks. ___ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
Re: [computer-go] creating a random position
At 21:54 08/07/2007, you wrote: I don't have such algorithm, you can count legal positions like: http://www.lysator.liu.se/~gunnar/legal.pike.txt Modifying it could provide some way select random position atleast for small boards. Ported that for java but not studied much of it yet, intresting anyway. t. Harri This page seems more up to date, and links a paper http://homepages.cwi.nl/~tromp/go/legal.html ___ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
Re: [computer-go] Re: 9x9 games wanted
It might be worth asking the administrators of some go servers if they would be prepared to give you copies of some games. At 17:09 06/07/2007, you wrote: I will play with Suzie at the forthcoming European Go championship in Villach/Austria some 9x9 demonstration matches against everybody who wants to play. I want to prepare an opening book and I am looking for a 9x9 games collection. So far I have only found in total 244 games, which is for a book much too less (I am used to have the CB-Megabase). Is there a larger collection with at least = 5 Amateur Dan Level available? If the price is reasonable, I am willing to pay for a professionally made collection. Chrilly ___ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/ -- This email has been verified as Virus free Virus Protection and more available at http://www.plus.net ___ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
[computer-go] article in the Times
It isn't a very good article in my opinion, but for what it's worth. http://www.timesonline.co.uk/tol/comment/columnists/ben_macintyre/article2002699.ece ___ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
RE: [computer-go] Sylvain's results
There are multiple possible definitions of what it means for a player to be the same strength on two different sized boards. It is impossible to pit a 9x9 player against a 19x19 player. If two people use different definitions of 'same strength', they are bound to disagree about which size people are better at. Off the top of my head, 'same strength'could be defined as i) Same amount of experience of both games, ii) Drops the same number of points over the course of the game when playing a perfect player, iii) As ii), but scaled for the board area, iv) Typically reads the same number of moves, v) Same win rate on average when playing typical human players, vi) Same win rate on average when playing Gnu Go. At 05:42 12/04/2007, you wrote: On Wed, 2007-04-11 at 17:29 -0700, David Fotland wrote: No, humans are much weaker on 9x9 than on 19x19. David, I saw this on Sensei's Library that indicates larger boards are harder: [ snip ] In [ext]The Theory Practice of Go, Korschelt describes an experimental 21x21 goban that he constructed and turned over to his Master, Murase Shuho, for testing. He describes a sample game that was played out to about 130 moves before ending. Only the first 57 were shown. Korschelt remarked that the game took on a freer and more deeply involved character, but ... at the same time the difficulty of keeping command of the game grew at an extraordinary rate. He goes on to note that on a 19x19 board, too many unexpected situations turn up for beginners, and speculates that if the board were to increase to 23x23, not even the best players could any longer maintain a comprehensive view of the countless possible combinations. [ snip ] This seems to also match my intuition, even though I'm not as good a player as you are. If you extrapolate backwards, to 7x7, 5x5, 3x3 it seems clear that smaller boards are less complex and therefore easier to master. I cannot believe 9x9 is harder than 19x19 and I don't care how strong the player is who says that - I don't believe it. - Don ___ computer-go mailing list [EMAIL PROTECTED] http://www.computer-go.org/mailman/listinfo/computer-go/ -- This email has been verified as Virus free Virus Protection and more available at http://www.plus.net ___ computer-go mailing list [EMAIL PROTECTED] http://www.computer-go.org/mailman/listinfo/computer-go/
Re: [computer-go] The dominance of search (Suzie v. GnuGo)
Thank you Sylvain for conducting these experiments. We have had some very enlightening results posted here recently in my opinion. I have to admit, I'm surprised at how well the program seems to scale. Fortunately, I didn't make a bet. :) Taking for granted that these results indeed show what they seem to, and combining them with the success of Monte-Carlo methods on 7x7 and 9x9 boards, I'll have to change my opinion about the future of computer go quite radically. It now seems believable to me that computer go will go the way of computer chess, and within the next decade or so as well. Or maybe Chrilly will make a monster go machine even before that. Could somebody comment please on the likely usefulness of massively parallel machines to UCT-like algorithms. Thanks again. Tom. At 21:12 10/04/2007, you wrote: Hello, 2007/4/6, Tom Cooper mailto:[EMAIL PROTECTED][EMAIL PROTECTED]: My guess is that the complexity of achieving a fixed standard of play (eg 1 dan) using a global alpha-beta or MC search is an exponential function of the board size. (...) To some extent, this is testable today by finding how a global search program's strength scales with board size and with thinking time I have experiments of MoGo's playing strength against a fixed player (Gnugo 3.7.10 level 8) on different board sizes and different thinking times. Of course, to meet your statement we have here to assume that the level of gnugo level 8 is a constant with the board size. The results are that in order to keep the same winning rate, you have to increase the number of simulations by something a little larger than linear in the board area. From 9x9 to 13x13, you need something like 3 times more simulations for the same winning rate. Same thing from 13x13 to 19x19. As the time of one simulation is linear in the board area, to keep the same level you have to give a time which increases as power ~2.5 of the board area. So between 9x9 and 19x19, you have to give 32x more time per move for the same play level (always defined as winning rate against gnugo). This is far from being exponential. (maybe if it was exponential, there would be little interest to work on 9x9 go). Sylvain ___ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/ ___ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
Re: [computer-go] The physics of Go playing strength.
Thanks dons for producing these fascinating results. I hope that when you have finished the study, you will show us not just this graph, but also the game results (number of wins) that it is derived from. At 02:05 08/04/2007, you wrote: A few weeks ago I announced that I was doing a long term scalability study with computer go on 9x9 boards. I have constructed a graph of the results so far: http://greencheeks.homelinux.org:8015/~drd/public/study.jpg ___ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
Re: [computer-go] The physics of Go playing strength.
The discussion here http://senseis.xmp.net/?EloRating suggests that the difference between beginners and top players in go is about 3000 ELO on a 19x19 board. This difference is very dependent on the board size. I can think of a naive argument that this difference should scale linearly with the (linear) size of the board, that is as the square-root of the area of the board. At 08:56 08/04/2007, you wrote: According these results the slope is considerable greater than in chess. In the classical experiment of Ken Thompons searching 1 ply deeper is worth about 200 Elo. 1 ply corresponds to 5-6 times longer/faster. In 9x9 already a factor of 2 gives the same improvement. This is really remarkable. Another explanation would be, that 100 Elo have in Go a different meaning than in chess. It is often argued that the distance between week and stronger player is much greater in Go than in Chess. In chess the distance between an average club player and top humans is about 1000 Elo. Maybe in Go its 2000 Elo?? In chess the green level-11 version would have world-champion level. Is it just enough to make a 2 million playouts version to beat the top-Dans in 9x9? Is it that easy? Just build a special purpose chip like ChipTest aka Deep Blue. Or implement it on a cluster. Or just wait a few years on do it on the PC. Or a playstation. Chrilly Is there any notion of the Elo rating of a professional Go player. In chess terms the - Original Message - From: Don Dailey [EMAIL PROTECTED] To: computer-go computer-go@computer-go.org Sent: Sunday, April 08, 2007 3:05 AM Subject: [computer-go] The physics of Go playing strength. A few weeks ago I announced that I was doing a long term scalability study with computer go on 9x9 boards. I have constructed a graph of the results so far: http://greencheeks.homelinux.org:8015/~drd/public/study.jpg Although I am still collecting data, I feel that I have enough samples to report some results - although I will continue to collect samples for a while. This study is designed to measure the improvement in strength that can be expected with each doubling of computer resources. I'm actually testing 2 programs - both of them UCT style go programs, but one of those programs does uniformly random play-outs and the other much stronger one is similar to Mogo, as documented in one of their papers. Dave Hillis coined the terminolgoy I will be using, light play-outs vs heavy play-outs. For the study I'm using 12 versions of each program. The weakest version starts with 1024 play-outs in order to produce a move. The next version doubles this to 2048 play-outs, and so on until the 12th version which does 2 million (2,097,152) playouts. This is a substantial study which has taken weeks so far to get to this point. Many of the faster programs have played close to 250 games, but the highest levels have only played about 80 games so far. The scheduling algorithm is very similar to the one used by CGOS. An attempt is made not to waste a lot of time playing seriously mis-matched opponents. The games were rated and the results graphed. You can see the result of the graph here (which I also included near the top of this message): http://greencheeks.homelinux.org:8015/~drd/public/study.jpg The x-axis is the number of doublings starting with 1024 play-outs and the y-axis is the ELO rating. The public domain program GnuGo version 3.7.9 was assigned the rating 2000 as a reference point. On CGOS, this program has acheived 1801, so in CGOS terms all the ratings are about 200 points optimistic. Feel free to interpret the data any way you please, but here are my own observations: 1. Scalability is almost linear with each doubling. 2. But there appears to be a very gradual fall-off with time - which is what one would expect (ELO improvements cannot be infinite so they must be approaching some limit.) 3. The heavy-playout version scales at least as well, if not better, than the light play-out version. (You can see the rating gap between them gradually increase with the number of play-outs.) 4. The curve is still steep at 2 million play-outs, this is convincing empirical evidence that there are a few hundred ELO points worth of improvement possible beyond this. 5. GnuGo 3.7.9 is not competive with the higher levels of Lazarus. However, what the study doesn't show is that Lazarus needs 2X more thinking time to play equal to GnuGo 3.7.9. This graph explains why I feel that absolute playing strength is a poor conceptual model of how humans or computers play go. If Lazarus was running on the old Z-80 processors of a few decades ago, it would be veiewed as an incredibly weak program, but running on a supercomputer it's a very strong program. But in either case it's the SAME program. The difference is NOT the amount of work each system is capable of, it's just that one takes longer to
Re: [computer-go] The dominance of search (Suzie v. GnuGo)
My guess is that the complexity of achieving a fixed standard of play (eg 1 dan) using a global alpha-beta or MC search is an exponential function of the board size. For this guess, I exclude algorithms that have a tactical or local component. If this guess is correct then, even if Moore's law remains in force, this kind of program should not reach dan level on a 19x19 board within 20 years. To some extent, this is testable today by finding how a global search program's strength scales with board size and with thinking time. For example, results in which Suzie had a week to play a 13x13 game would be interesting. I don't mean to imply by this message that I think I am particularly well qualified to have an opinion on this matter, but when someone writes something that surprises me, I'm inclined to argue :) On 13x13 and especially 19x19 Suzie is still weaker than Gnu-Go. I think the hardware is still too weak to establish the same dominance of search for larger board-sizes. But thats only a matter of time or of a few million $ to build (with Chris Fant) a Go-Chip. Actually about 100.000 Euro for an FPGA based project would be sufficient. Chrilly Donninger ___ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
Re: [computer-go] computer go documentation issues
I agree. The feel of sensei's and wikipedia are completely different. Most of the content on sensei's is too informal for wikipedia, and I think it would get deleted if it was put there, despite this content being very worthwhile. On the other hand, wikipedia is the ideal place for a short authoritative introduction. At 13:25 19/03/2007, dons wrote: Sensei and Wikipedia serve somewhat different purposes and I believe they should both be kept up to date. I don't believe the detail of Sensei's Library should be covered by Wikipedia. If I first wanted to get acquainted with some subject I might look it up in an encyclopedia to get an overview, then I would look for more detailed information in a book or other publications. I think this is how the two should work together. - Don ___ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
Re: [computer-go] Useless moves in the endgame
At 16:20 09/01/2007, you wrote: i'd like to follow this up by saying that i'm interested to see if anyone has compared winning percentage in the following two situations: i) maximize probability of win ii) maximize probability of win until p_win 1-eps, then maximize total score among all moves that give 1-eps probability of winning. if ii) gives a lower percentage of wins than i), it'd be an interesting result in its own right, and if it doesn't, then there's a simple formula for getting a lot more resignations out of your opponents (not to mention, it'll give the impression that your program is incredibly strong whenever it wins). To me it seems sensible to maximize a different measure. I would be interested to know how the following scheme performs. Choose the starting move which maximizes [sum (s(n)+k*w(n))]/N, where N is the number of games with this starting move, n takes the values 1...N: the sum is over these values, k is a non-negative constant, s(n)is the net score of the bot (eg the bot's area minus its opponent's area) in game n, and w(n) is 1 if the bot wins game n and 0 if it loses. When k is very large, this scheme should essentially just count the wins, like traditional monte-carlo. Smaller values of k will encourage the bot to try to accumulate more points unless that makes a significant difference to the winning percentage. A value of k around 300 might be a sensible value. The computer might then trade a 30 point group for a 1% chance of winning. I suspect that this scheme will still reduce the bot's winning percentage, but that there might be a value of k that will improve the 'look' of the bot's play while only making a negligible difference to the winning percentage. When working out s(n), it makes no difference to the play if komi is taken into account, as long as a consistent method of evaluating the score is used. ___ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
Re: [computer-go] Re: Interesting problem
At 23:17 03/01/2007, Don wrote: David, I thought of another way to put it which I think, in a way, defines the difference in the rule-sets. You are playing a game, and you think the opponent group is dead. But you are not 100 percent sure. What do you do? Chinese puts the emphasis on the actual truth of the situation. Japanese makes you gamble, and penalizes you for being wrong. It makes your opinion about the situation become a factor in the final result instead of the board position and your play leading up to it. Don, I can see that chinese rules let a player try a speculative invasion inside his opponents territory at the end of the game without risk, but you seem to be saying more than this. Could you give a 5x5 example or two please? I had heard that in some sense, chinese rules require more sophisticated understanding for perfect play. It might be best to construct the example by playing a pretend game so that each player has played the fair number of stones. Thanks ___ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
Re: [computer-go] When is Pass the best move?
At 01:54 23/10/2006, you wrote: There was a posting on this list with an example of a (contrived?) situation where sacrificing a pass-alive group is appropriate, in order to win a ko that is more valuable. Is even #1 100% admissible? Weston I must have missed this, and find it surprising. Can anyone remember the example? ___ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
