Re: Some personal thoughts on 9x9 computer go

That was very interesting, thanks for sharing!

On Fri, Mar 2, 2018 at 9:50 AM, <[email protected]> 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
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