I 100% agree with Don, dynamic komi just cant be the right approach in my
opinion.
One idea I just have is this :
In the tree search part, instead of using a rule wich converges to MAX, use a
rule wich converges to alpha*MAX + beta*AVERAGE. Do this only for plies where
it is the weaker player turn (the player who benefits from handicap stones)
When beta is high, it may simulate the fact that the weak player cant actualy
read out sequences reliabily, thus increasing the chances of succes of the
stronger player.
Just a wild guess anyway...
----- Original Message -----
From: Don Dailey
To: computer-go
Sent: Wednesday, August 12, 2009 11:11 PM
Subject: Re: [computer-go] Dynamic komi at high handicaps
The problem with MCTS programs is that they like to consolidate. You set
the komi and thereby give them a goal and they very quickly make moves which
commit to that specific goal. Commiting to less than you need to actually win
will often involve sacrificing chances to win. Sometime it won't, but you
cannot have a scalable algorithm which is this arbitrary.
However, if the handicap is too high, the program thinks every line is a loss
and it plays randomly. That's why we even consider doing this.
Dynamically changing komi could be of some benefit in that situation if there
is no alternative reasonable strategy, but it does not address the real
problem - which is what I call the "committal consolidation" problem. You
are giving the program an arbitrary short term goal which may, or may not be
compatible with the long term goal of winning the game. Whether it's
compatible or not is based on your own credulity - not anything predictible or
that you can scale. And as the base program gets stronger this aspect of the
program becomes more and more of a wart.
If this can be made to work in the short term, it should be considered a
temporary hack which should be fixed as soon as possible.
We have to think about this anyway sooner or later because if programs
continue to develop and the predictive ability of the playouts and tree search
gets several hundred ELO better, these programs may start to see more and more
positions as either dead won or dead lost. I'm sure we will want some kind
of robust mechanism for dealing with this which is better at estimating chances
that the opponent will go wrong as opposed to doing something that is a random
benefit or hindrance.
- Don
2009/8/12 terry mcintyre <terrymcint...@yahoo.com>
Ingo suggested something interesting - instead of changing the komi
according to the move number, or some other fixed schedule, it varies according
to the estimated winrate.
It also, implicitly, depends on one's guess of the ability of the opponent.
An interesting test would be to take an opponent known to be weaker, offer
it a handicap, and tweak the dynamic komi per Ingo's suggestion. At what
handicap does the ratio balance at 50:50? Can the number of handicap stones be
increased with such an adaptive algorithm?
Even better, play against a stronger opponent; can one increase the win
rate versus strong opponents?
The usual range of computer opponents is fairly narrow. None approach
high-dan levels on 19x19 boards - yet.
Terry McIntyre <terrymcint...@yahoo.com>
“We hang the petty thieves and appoint the great ones to public office.” --
Aesop
----------------------------------------------------------------------------
From: Brian Sheppard <sheppar...@aol.com>
To: computer-go@computer-go.org
Sent: Wednesday, August 12, 2009 12:33:13 PM
Subject: [computer-go] Dynamic komi at high handicaps
>The small samples is probably the least of the problems with this. Do you
>actually believe that you can play games against it and not be subjective
in
>your observations or how you play against it?
These are computer-vs-computer games. Ingo is manually transferring moves
between two computer opponents.
The result does support Ingo's belief that dynamic Komi will help programs
play high handicap games. Due to small sample size it isn't very strong
evidence. But maybe it is enough to induce a programmer who actually plays
in such games to create a more exhaustive test.
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