On Fri, 2008-08-29 at 10:00 +0200, Magnus Persson wrote:
> Some months ago someone published a set of L&D problems made for MCTS
> programs. Going through this I found a lot of serious bugs in Valkyria
> where overly aggressive pruning removed tesujis (tesuji = move that
> normally should be
Some months ago someone published a set of L&D problems made for MCTS
programs. Going through this I found a lot of serious bugs in Valkyria
where overly aggressive pruning removed tesujis (tesuji = move that
normally should be pruned).
After that Valkyria improved perhaps 50-100 Elo. But I
On Thu, 2008-08-28 at 17:00 -0400, Robert Waite wrote:
> * If my ego were hurt by the fact that Mogo scales better, I
> * could easily construct a theory that explained it away. This is what we
> * tend to do when we don't want to believe something.That's what I
>
> * think is being done with
>
> The scary strong Rybka program claims to be weak tactically. The
> developers say that problem solving skill does not correlate strongly
> with playing strength and they don't tune or care about that.
I've found the same thing for go. I have a large tactical problem set, and
I use it
On Thu, 2008-08-28 at 16:26 -0400, Don Dailey wrote:
> Steve,
>
> If you go here:
>
>
> http://cgos.boardspace.net/9x9/digest.txt
>
> http://cgos.boardspace.net/13x13/digest.txt
>
> http://cgos.boardspace.net/19x19/digest.txt
>
>
> you will get a compact digest of all games played that
Steve,
If you go here:
http://cgos.boardspace.net/9x9/digest.txt
http://cgos.boardspace.net/13x13/digest.txt
http://cgos.boardspace.net/19x19/digest.txt
you will get a compact digest of all games played that is up to date
within a few hours at any particular moment. With awk, sort, gr
out of curiosity, can you estimate the largest number of opponents
that all played each other a reasonable number of times? (i.e. what's
the largest subset of opponents and number of games that you
can choose so that everyone started playing everyone else in
the subset without anyone leaving for g
On Thu, 2008-08-28 at 08:37 -0700, terry mcintyre wrote:
> Regarding a rating system which provides more dimensions, may I suggest a
> test suite of problems at different levels?
>
> Convert life-and-death problems to "solve this problem or lose the game"
> situations which can be properly app
this approach would also severely limit the number
of players that could be involved in the rating system,
since it would require manipulating an 2*(N choose 2)
matrix, where N is the number of players involved.
s.
On Thu, Aug 28, 2008 at 12:35 PM, steve uurtamo <[EMAIL PROTECTED]> wrote:
> you c
If you ever want to try, I can give you the data for cgos in compact
form that you can experiment with (one line per game - 2 names and 1
result + date) or you can simply extract them from the archived games.
- Don
On Thu, 2008-08-28 at 17:44 +0200, Rémi Coulom wrote:
> This was my post about
you could use HMMs as long as you
didn't mind retraining (and thus starting your ratings
system over from scratch) every time you added or
subtracted a new player. it'd be relatively fast in any case.
s.
On Thu, Aug 28, 2008 at 11:44 AM, Rémi Coulom
<[EMAIL PROTECTED]> wrote:
> This was my post
On Thu, 2008-08-28 at 17:44 +0200, Rémi Coulom wrote:
> This was my post about multi-dimensional Elo:
> http://www.mail-archive.com/computer-go@computer-go.org/msg06267.html
>
> I have not tried it since that time.
Wow, I can't believe I forgot about this one. It was less than a year
ago that yo
This was my post about multi-dimensional Elo:
http://www.mail-archive.com/computer-go@computer-go.org/msg06267.html
I have not tried it since that time.
Rémi
___
computer-go mailing list
computer-go@computer-go.org
http://www.computer-go.org/mailman/li
Regarding a rating system which provides more dimensions, may I suggest a test
suite of problems at different levels?
Convert life-and-death problems to "solve this problem or lose the game"
situations which can be properly appreciated by monte carlo programs, and make
a guesstimate of the elo
Oh yes, the graphs are still there:
http://cgos.boardspace.net/study/
http://cgos.boardspace.net/study/13/
- Don
On Thu, 2008-08-28 at 10:10 -0400, Don Dailey wrote:
> On Thu, 2008-08-28 at 09:38 +0200, Rémi Coulom wrote:
> > Don Dailey wrote:
> > > I don't really believe the ELO model is
On Thu, 2008-08-28 at 08:21 -0400, Michael Williams wrote:
> Don Dailey wrote:
> > Assuming a program
> > doesn't forfeit in stupid ways, they NEVER have bad days, wake up on
> > the wrong side of the bed, get in a fight with their spouse, get
> > inspired to play well on a particular day or de
On Thu, 2008-08-28 at 09:38 +0200, Rémi Coulom wrote:
> Don Dailey wrote:
> > I don't really believe the ELO model is "very wrong." I only believe
> > it is a mathematical model that is "somewhat" flawed for chess and
> > presumable also for other games. Do you have an alternative that might
>
Don Dailey wrote:
Assuming a program
doesn't forfeit in stupid ways, they NEVER have bad days, wake up on
the wrong side of the bed, get in a fight with their spouse, get
inspired to play well on a particular day or depressed on another day.
It doesn't feel pain, or pity, or remorse. And i
Don Dailey wrote:
I don't really believe the ELO model is "very wrong." I only believe
it is a mathematical model that is "somewhat" flawed for chess and
presumable also for other games. Do you have an alternative that might
be more accurate?
- Don
I don't have very precise data about
Eric,
Yes, I agree with everything you said, well put.
I believe computer programs are much more stable than human players
except for the forfeit problem you mentioned. Assuming a program
doesn't forfeit in stupid ways, they NEVER have bad days, wake up on
the wrong side of the bed, get in
On Thu, 2008-08-28 at 00:45 +0200, Rémi Coulom wrote:
> Don Dailey wrote:
> > On Wed, 2008-08-27 at 14:56 -0700, Bob Hearn wrote:
> >
> >> The MoGo team has said that MoGo wins 62% of its games against a
> >> baseline version when the processing power doubles. That's about
> >> half
> >> a s
When you measure win rates against players with a given rating, you
measure both how well player strength predicts probability of winning,
and how accurately the ratings reflect player strength. Sometimes the
ratings are quite inaccurate. This causes win rates to regress towards
50%. If you can inc
- Original Message
> From: Rémi Coulom <[EMAIL PROTECTED]>
> According to my experience with Go data, it is not possible to give the
> value of one stone in terms of Elo ratings. For weak players, one stone
> is a lot less than 100 Elo. For stronger players, it may be more.
>
> Also,
On Wed, Aug 27, 2008 at 5:45 PM, Rémi Coulom <[EMAIL PROTECTED]>wrote:
> Don Dailey wrote:
>
>>
>> Yes, I believe it does generalize on average.
>> This data matches my 13x13 study pretty closely, about 62% give or take
>> for each doubling. That is about 90 ELO or so. I have heard that
>>
Don Dailey wrote:
On Wed, 2008-08-27 at 14:56 -0700, Bob Hearn wrote:
The MoGo team has said that MoGo wins 62% of its games against a
baseline version when the processing power doubles. That's about
half
a stone (if you assume you can generalize to human opponents).
Yes, I believe
On Wed, 2008-08-27 at 14:56 -0700, Bob Hearn wrote:
> The MoGo team has said that MoGo wins 62% of its games against a
> baseline version when the processing power doubles. That's about
> half
> a stone (if you assume you can generalize to human opponents).
Yes, I believe it does generalize on
On Aug 27, 2008, at 2:48 PM, Don Dailey wrote:
On Wed, 2008-08-27 at 13:20 -0700, Bob Hearn wrote:
In principle MoGo ought to be about a stone (or slightly more) weaker
with 1/5 the processing power, which is consistent with 2-3d against
Kim and 1-2d against the 6d.
I thought a doubling was w
On Wed, 2008-08-27 at 13:20 -0700, Bob Hearn wrote:
> In principle MoGo ought to be about a stone (or slightly more) weaker
> with 1/5 the processing power, which is consistent with 2-3d against
> Kim and 1-2d against the 6d.
>
> I watched both games, and MoGo did seem stronger to me against K
On Wed, 2008-08-27 at 16:08 -0400, Robert Waite wrote:
> * You really can't conclude much about any mogo strength improvement from just
> * one game.
>
> It is true that you can't make a conclusion.. but you can draw some
> information
> from two games. I would think it is statistically unlikely
In principle MoGo ought to be about a stone (or slightly more) weaker
with 1/5 the processing power, which is consistent with 2-3d against
Kim and 1-2d against the 6d.
I watched both games, and MoGo did seem stronger to me against Kim...
but then, I knew in advance the processing power in e
You really can't conclude much about any mogo strength improvement from just
one game.
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of Robert Waite
Sent: Wednesday, August 27, 2008 7:54 AM
To: computer-go@computer-go.org
Subject: [computer-go] yet a mogo vs human game
* - MoG
What were the software improvements? Were they related to the code distributing
the work, or to the actual game playing/move selection code?
Jim
- Original Message
From: Robert Waite <[EMAIL PROTECTED]>
To: computer-go@computer-go.org
Sent: Wednesday, August 27, 2008 9:54:14 AM
Subj
More informations later, but we can already say that:
- the opponent is 6D
- MoGo was using 5% of Huygens (instead of 25% against Kim);
- there were some software improvements
- MoGo won 2 out of 3 games in 9x9 (even games)
- MoGo won with handicap 5 in 19x19 against the 6D player
- games can be fo
Yes, and then 19x19 with handicap.
>
> On Aug 25, 2008, at 10:47 PM, Olivier Teytaud wrote:
>
>> Just for information, mogo will play in a few minutes (on Kgs /
>> computer-go) some games
>> against high level humans.
>>
>
> MogoTitan is playing 9x9 against nutngo ?
>
> Christoph
>
> ___
On Aug 25, 2008, at 10:47 PM, Olivier Teytaud wrote:
Just for information, mogo will play in a few minutes (on Kgs /
computer-go) some games
against high level humans.
MogoTitan is playing 9x9 against nutngo ?
Christoph
___
computer-go mailing li
35 matches
Mail list logo
