[Computer-go] Narrow wins

David Fotland fotland at smart-games.com
Sun Jun 9 12:23:39 PDT 2013

Dynamic komi and some other tricks work quite well.  Thanks to Ingo for
pushing dynamic komi until I figured out how to make it work well.  Often
the playout have some bias due to a misread in a fight, so it's important
for the bot to keep its lead.


If you look at kgs games with strong bots, 0.5 wins are now very rare.

Example: manyfaces: http://www.gokgs.com/gameArchives.jsp?user=manyfaces




From: computer-go-bounces at dvandva.org
[mailto:computer-go-bounces at dvandva.org] On Behalf Of Don Dailey
Sent: Saturday, June 08, 2013 12:29 PM
To: computer-go at dvandva.org
Subject: Re: [Computer-go] Narrow wins




To a bot this always comes down to risk analysis and it's no different than
what we do.   In fact you just did your own risk analysis here of the
computers play and have decided that it does risk analysis wrong so your
thinking is no different from the bots.    


To me the biggest issue isn't winning by 0.5 but not fighting when it's
losing.    I have a feeling that most of this is not about improving the
play of the bot although it's always cast that way,  but in reality it's
about not offending our own sensibilities.    We just hate to see a bot win
by 0.5 when it probably could have won the entire board or most of it.
And of course not fighting when losing is not much fun to watch when it may
still have a realistic chance of winning due to an opponents mistake.   


The standard proposed solution is komi-manipulation in one way or another.
But what we are probably really after is something to do with "opponent
modeling",  giving the computer more of a fighting spirit instead of playing
overly logical.    I sense there must be a better solution as
komi-manipulation just seems to me to be really illogical.    It's like
fixing the books because we don't like the numbers.   


I have failed to keep up much with computer go since getting involved with
my chess program Komodo.    But I see that this problem is still being
talked about.   Has there been any progress on this front?        If this
were computer chess we would modify the evaluation function,  but the
evaluation function in GO is really based on the play-outs.      At the end
of every playout we have a final board positions that returns some
information.   Some positions will return bigger "wins" than others (or
smaller losses) but we know scoring this way is terrible.    But maybe that
information can be effectively used to shape the tree towards positions that
win bigger?   Sometimes it's a matter of which node to explore first - so it
is quite likely that at least in some cases the computer does not make any
distinction between a big or small win and thus we could possibly push it in
that direction in the tree part?     Maybe that is naive,  I don't know.   


So what is the state of the art on this now? 







On Sat, Jun 8, 2013 at 2:21 PM, Stefan Kaitschick
<stefan.kaitschick at hamburg.de> wrote:

Humans may be predisposed to the fallacy of greed.
But bots have there own fallacies. Happily winning by 0.5, when a
higher win at almost the same perceived risk is available, is a kind
of full knowledge fallacy. A bot can lose an easily won game by
merrily giving away everything but the last half point, then have the
last point stolen from him by a single bot misread. So if you feel
that the bot you're playing against is misjudging a particular
situation, don't spring in on him unless that will put you in the
lead. Otherwise, it's better to let him hand over other points that
you need first. The bot might fix his problem, but that risk is
preferable to having the bot go into business again when it is still

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