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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