Quoting Yamato <[EMAIL PROTECTED]>:
In other words UCT works well when evaluation/playouts is/are strong. I
believe
there are still improvements possible to the UCT algorithm as shown by the
recent papers by Mogo and Crazystone authors, but what really will make a
difference is in the quality in
George Dahl wrote:
Is it still cheating if the program learns and discover's
the patterns itself? Then isn't it just precomputing a few
things?
I agree. Many chess and go grand masters, e.g. Capablanca
are supposed to have learned the game as children by watching
others play. I do intensive
Hi, Magnus
Magnus Persson wrote:
Weak tactics is a problem of the playouts in my opinion.
UCT as a general search method has thus little to do with
ladders and other game specific details. If there are no
tactical mistakes in the playouts the problems disappear.
Also tactics has a large impa
Quoting Jacques Basaldúa <[EMAIL PROTECTED]>:
Hi, Magnus
Magnus Persson wrote:
Weak tactics is a problem of the playouts in my opinion. UCT as a
general search method has thus little to do with ladders and other
game specific details. If there are no tactical mistakes in the
playouts the prob
Don Dailey wrote:
I have posted before about the evils of trying to extract
knowledge from human games. I don't think it is very effective
compared to generating that knowledge from computer games for
several reasons.
I would agree if we could have quality games played by computers.
In 19x19
Peter Drake wrote:
1) If the computation necessary to find better moves is too
expensive, performing many "dumb" playouts may be a better investment.
2) If the playouts are too deterministic, and the moves are merely
pretty good, the program may avoid an important move and thus
misjudge t
On Thu, 2007-07-05 at 09:53 +0200, Magnus Persson wrote:
> A second way of formulating this is that there are no rules without
> exceptions
> in go. Thus for every pattern added one creates problems, and one
> might
> need to
> discover those exceptions to get the benefits. And then there are of
>
But you can improve the prior probabilities of your search function by
remembering shapes (hopefully more abstract ones in the future,
including more knowledge about the neighbourhood) that seemed like good
moves before, so I don't share your opinion.
Whether or not this knowledge shout also be
On Thu, 2007-07-05 at 09:47 +0100, Jacques Basaldúa wrote:
> Don Dailey wrote:
>
> >I have posted before about the evils of trying to extract
> >knowledge from human games. I don't think it is very effective
> >compared to generating that knowledge from computer games for
> >several reasons.
Quoting Jacques Basaldúa <[EMAIL PROTECTED]>:
Low variance is the clue for improvement.
Here I agree with you completely.
I put it in my own words:
As I see it MC-evaluation has a signal hidden by a lot of noise.
This has some consequences.
In order to evaluate a position correctly a lot o
>> In other words UCT works well when evaluation/playouts is/are
>> strong. I
>> believe
>> there are still improvements possible to the UCT algorithm as
>> shown by the
>> recent papers by Mogo and Crazystone authors, but what really will
>> make a
>> difference is in the quality in the playouts.
The trouble with emulating good moves in pro games is that we don't know why
those moves are good.
Lately, I've been studying joseki, and I find that it's hard to really know a
joseki until you know why non-joseki moves are bad - and why moves which are
locally joseki may be bad in relation to
Seems like it should be up to the person in the other environment
to adapt your
successful algorithm (and notation/terminology) to their environment.
But how do the other people in other environments find out about the
algorithm? And find out that it is something they could use in their
ow
On Thu, 2007-07-05 at 10:50 -0600, David Silver wrote:
> We tried the whole spectrum from completely random to completely
> deterministic playouts, but we never came close to the performance of
> the "dumb" playouts!
I don't understand - I though Mogo wasn't using "dumb" play-outs?
> We have see
One of my favorite observations about Go is that expert play tends
to be "on the edge of catastrophy".
By playing better moves on the average, you become more vulnerable
to the occasional misstep.
If a program is not very good, random better or worse moves do not have
much effect. If the prog
One of my favorite observations about Go is that expert play tends
to be "on the edge of catastrophy".
By playing better moves on the average, you become more vulnerable
to the occasional misstep.
If a program is not very good, random better or worse moves do not have
much effect. If the prog
- Original Message
From: Dave Dyer <[EMAIL PROTECTED]>
> One of my favorite observations about Go is that expert play tends to be "on
> the edge of catastrophe".
> By playing better moves on the average, you become more vulnerable to the
> occasional misstep.
> If a program is not
We have encountered this consistently in our non-MC/UCT program.
Things that "fix an obvious problem" lead to unintended consequences
that sometimes take weeks to tease apart. So far we have been able to
understand how this comes about in each situation, but still have
little ability to pre
> From: Dave Dyer <[EMAIL PROTECTED]>
> One of my favorite observations about Go is that expert play tends
> to be "on the edge of catastrophy".
I've always thought about it as professionals trying to playing on the
"edge-of-chaos." Go is an interesting game in that regard since at least early
Darren Cook wrote:
I've been toying with the idea of having a set of playout algorithms and
allowing black and white to choose different algorithms in that playout.
(The idea came from trying to think how I could apply genetic
algorithms to UCT playouts.)
Here's how it would work. Assume you ha
Don wrote:
> Presumably, if you run 1000 random play-outs from a given position you
> will get a fair indication of "how good" the position is.
>
> But what if you are able to prune out many of the bad moves in that
> simulation? Would this improve the accuracy of the simulation?
>
> Prob
On Jul 5, 2007, at 5:01 PM, Jason House wrote:
Darren Cook wrote:
I've been toying with the idea of having a set of playout
algorithms and
allowing black and white to choose different algorithms in that
playout.
I've been thinking about that type of thing too. It seems like
Remi's frame
Darren Cook wrote:
> I've been toying with the idea of having a set of playout
> algorithms and
> allowing black and white to choose different algorithms in that
> playout.
Sounds like a great opportunity for parallelization too.
Anyone using Amazon EC2?__
>There is one other issue I have seen that is similar. Sometimes
>Lazarus will play a move that doesn't hurt nor help it's position.
>It's not a wasted move because the opponent must respond or else lose.
>An example is a simple self-atari which itself is a direct threat. The
>opponent is force
I think one of the problems is in testing. Currently we have almost
no way to judge whether a improvement is good or bad, other than
playing a lot of games against GNU Go. It takes very long time and
seems inefficient. Moreover, even it may not be a very good method.
GNU Go often cannot respond to
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