It's not available online, but I will send it if someone ask me to.
(in private e-mail)
Lukasz
2009/10/13 Petr Baudis pa...@ucw.cz:
On Fri, Sep 25, 2009 at 11:51:21AM +0200, Łukasz Lew wrote:
I tried CRAVE in my master thesis 4 years ago. The context was a
growing decision tree.
It didn't
http://users.soe.ucsc.edu/~dph/mypubs/AMAFpaperWithRef.pdf
Cheers,
David
On 25, Sep 2009, at 12:34 PM, Peter Drake wrote:
Yes. I believe Fuego does this. See also Helmbold and Parker-Wood,
All-Moves-As-First Heuristics in Monte-Carlo Go:
(Does anyone have a URL for this one? I can't seem
Here's a suggestion to extend RAVE to better handle it:
There are 20 points within keima distance of any point not close to the
edge.(5*5 without the corners)
When RAVE values are backed up, they are put into the category defined by
the previous opponents move.
(21 categories, 20 + other. All
Stefan Kaitschick wrote:
Here's a suggestion to extend RAVE to better handle it:
There are 20 points within keima distance of any point not close to the
edge.(5*5 without the corners)
When RAVE values are backed up, they are put into the category defined by
the previous opponents move.
(21
MCTS, even though it walks to the end of the earth, has it's own horizon
effect.
The name is more fitting for depth-limited alpha-beta search ofcourse.
It's a kind of procrastination. Finding a lot of useless things to do before
admitting
an undesirable, but unavoidable consequence.
Even if a
Brian Sheppard wrote:
Fuego uses a lower weight for distant moves than for nearby moves.
I suspect that isn't much better than using uniform weight. I am
hope that Martin or Markus will comment.
I measured a winning rate of 55.1(+-0.8)% of Fuego with weighted RAVE
updates vs. the version
Message-
From: computer-go-boun...@computer-go.org [mailto:computer-go-
boun...@computer-go.org] On Behalf Of Peter Drake
Sent: Thursday, September 24, 2009 12:00 PM
To: Computer Go
Subject: [computer-go] Generalizing RAVE
RAVE is part of a larger family of algorithms. In general we can use
From: Brian Sheppard sheppar...@aol.com
I have another way to fail to improve on RAVE. :-)
Well, that's great news.
Thomas Edison was once asked if he felt discouraged by 10 thousand failed
experiments, and he said Not at all; I now know ten thousand ways
It is exactly the same as my thought. I also have tried CRAVE, but the
results were worse than normal RAVE.
While RAVE is a very efficient algorithm, it strongly limits scalability
of the program. It typically makes a fatal mistake in the position that
the order of moves are important. We
Yup, I tried something like that, too, with a similar lack of luck.
Peter Drake
http://www.lclark.edu/~drake/
On Sep 25, 2009, at 6:39 AM, Brian Sheppard wrote:
I have another way to fail to improve on RAVE. :-)
I tested a method that gives higher weight to recent RAVE data. The
method
Yes. I believe Fuego does this. See also Helmbold and Parker-Wood,
All-Moves-As-First Heuristics in Monte-Carlo Go:
(Does anyone have a URL for this one? I can't seem to find it online,
but I have a paper copy in front of me.)
Peter Drake
http://www.lclark.edu/~drake/
On Sep 25, 2009,
On Sep 24, 2009, at 8:45 PM, terry mcintyre wrote:
Indeed it is. How may a program reason about the order of moves? At
higher levels of play, the order of moves is often crucial.
I plan to try the following:
Store win and run counts for each move in the context of the two
previous moves.
it seems a little strange to me that all
outcomes below the current position are just added up.
Well, the weight is a second-order effect. The ratio of wins to losses
is primary. Any move that wins at any time in the future does suggest
that move might be winning now.
Changing the
RAVE is part of a larger family of algorithms. In general we can use
direct Monte-Carlo results (i.e., the move played directly from a
node) to determine the probability of winning after playing such a
move. The generalized RAVE (GRAVE?) family does this by including
(usually with some
Peter Drake wrote:
The more I study this and try different variants, the more impressed I
am by RAVE. Boards after the current board is a very clever way of
defining similarity. Also, recorded RAVE playouts, being stored in
each node, expire in an elegant way. It still seems that RAVE fails
Peter Drake wrote:
The more I study this and try different variants, the more impressed I
am by RAVE. Boards after the current board is a very clever way of
defining similarity. Also, recorded RAVE playouts, being stored in
each node, expire in an elegant
: [computer-go] Generalizing RAVE
RAVE is part of a larger family of algorithms. In general we can use
direct Monte-Carlo results (i.e., the move played directly from a
node) to determine the probability of winning after playing such a
move. The generalized RAVE (GRAVE?) family does
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