That was very interesting, thanks for sharing! On Fri, Mar 2, 2018 at 9:50 AM, <valky...@phmp.se> wrote:
> Hi, > somebody asked me to post my views on 9x9 go on the list based on my > experience with correspondence go on OGS and little Golem. > > I have been playing online correspondence tournaments since 2011 with > Valkyria which is a MCTS heavy playout MCTS using AMAF heavily tuned for > 9x9. Also with the kind Support of Ingo, I used to generate a lot of 9x9 > data for opening book preparations many years ago. > > http://www.littlegolem.net/jsp/games/gamedetail.jsp?gtid=go9&page=ch > > During these years I collected a opening book based on running Valkyria > with 2 thread something like 2 to 24 hours. I can do this because of a hash > table that works well. Valkyria is tuned to be very selective, so it > follows an iterative deepening algorithm where it searches for some time > and the discard the tree, storing the best move. In each iteration it will > start a new search with an empty tree, but will use the hash table to > research known position more efficiently. This way it can overcome the > problem that a MCTS fills memory very quickly. > > Valkyria has no stopping rule, so in the end it is mostly a hybrid > human/computer decision when to stop search. But most of the time I just > wait until it seems to converge on a single move with a clear winrate > advantage. For the openings I tend to do choose moves many times, mostly to > avoid lines where it has lost in the past, but I only choose moves it has > been investigated during iterative search. > > If I run Valkyria on 9x9 CGOS (2295 Bayes Elo) it is not very strong, but > against amateur humans on OGS and LG it has been very successful but not > unbeatable. > > On LG there is a player Gerhard Knop (who I think uses one or more > programs as support (or at least used to do I just read this indirectly > somewhere) which seems to be clearly strong right know. At least recently > he seems to be very good with white against Valkyria. > > So what have I found out about 9x9? > > I used to think that with the Opening book of Valkyria black is an easy > win with a komi less than 7.0. Since Gerhard Knop has been beating Valkyria > with white I changed into thinking black should be an easy win... but my > opening book is not very close to optimal play, it is just the playing > style of Valkyria. > > Other human players are playing very well too and it often happens that > Valkyria wins games which was evaluated as a loss despite the enormous > (well for a single PC guy, I am not Deepmind) computations behind all > moves. The strongest humans repeatedly play moves that Valkyria never read > deeply, even after 12 hours of computations, which turn out to be as strong > or better than the expected best move. > > So is 9x9 easier than 19x19? Yes of course... but it is not that easy. In > go there is the complexity of the number of legal moves but this is no > longer the big problem. Most moves can be searched safely very shallow or > not at all. In a well played 9x9 games it is a simultaneous problem of: > endgame, life and death, semeai with ko fights, subtle differences in move > ordering for forced moves etc. This give fighting lines that cannot be > reliably evaluated by MCTS until read 40-70 ply deep because all stones are > unstable. > > I have not yet trained a value network for 9x9 but I can imagine that it > might still be very hard to get close to perfect evaluation, so any engine > would still need to search very deep to play close to perfection. > > From the current surge of strong engines on CGOS 9x9 I just learned that > my engines is even further from perfect play that I previously thought > since there are no sign of these engines being near perfect play given > win/loss/jigo statistics. > > > TL;DR: > I did 9x9 computer go for many years and I think 9x9 go is much harder > than I originally thought. I am not ruling out a super strong 9x9 go > program appearing next weak. I am just saying that close to perfect is much > stronger than that I have seen so far. > > > Best > Magnus Persson > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go
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