The 15th World Computer-Chess Championship and the 12th Computer Olympiad will
be held in Amsterdam, the Netherlands in conjunction with the Computer Games
Workshop 2007 (CGW2007).
IBM, SARA (Academic Computer Centre Amsterdam) and NCF (Foundation of National
Computing Facilities) are enabling
Chrilly wrote:
I think on 9x9 the superiority of search based programms is now
clearly demonstrated. Its only the question if UCT or Alpha-Beta is
superior.
Hi Chrilly,
Thanks for your report.
The question of UCT versus Alpha-Beta is not open any more in my
opinion. The current state of the
Hello Sylvain
Sylvain Gelly wrote:
If the program blundered as you said and still wins, it means
that the program already won much earlier in the game.
You are totally right. I said that in my post already. But what the user
thinks is: 1. He was behind, but not desperately behind. 2. The
Hello Don
Don Dailey wrote:
Many people DO play chess after the game is over. They
continue to play a game long after they could have
resigned.
My example wasn't very good but I meant over literally.
= The king is captured (changing the rules a little).
How does Japanese make any
On Thu, Apr 05, 2007 at 10:49:05AM +0100, Jacques Basaldúa wrote:
Many users feel stolen by UCT programs. I have read that
in the KGS chatrooms. Normal users do not count with
+/- 0.5 point precision. They have the impression the
program blundered and they caught up. But when the
On Fri, 2007-04-06 at 12:46 +0100, Jacques Basaldúa wrote:
But what surprises me is that you pretend
that it has not to be done at all.
Why does it have to be done? Doesn't any player have a right
to play any way he wishes?
I agree that some people don't like it playing that way -
but that's
My guess is that the complexity of achieving a fixed standard of play
(eg 1 dan) using a global alpha-beta or MC search is an exponential
function of the board size. For this guess, I exclude algorithms
that have a tactical or local component. If this guess is correct
then, even if Moore's
Darren Cook wrote:
All except joseki-knowledge is board-size independent.
Maybe human player's adapt to different board sizes without
even noticing. But if you try to model strategy with algorithms
it is totally board size dependent.
The extreme case is 5x5 where black 3,3 claims the four
On Fri, 2007-04-06 at 13:48 +0100, Jacques Basaldúa wrote:
Darren Cook wrote:
All except joseki-knowledge is board-size independent.
Maybe human player's adapt to different board sizes without
even noticing. But if you try to model strategy with algorithms
it is totally board size
I would not be so quick to dismiss what Chrilly is saying. I have
noticed that over time, in science, things blend together. For
instance mtd(f) is a systematic way to think of aspiration search,
(tampering with the alpha/beta window in a search) and helps us to
appreciate how they are all
An imperfect evaluation has errors. Is the exact value of the error known? No.
Thus, it's random. :)
Daniel Liu
-Original Message-
From: [EMAIL PROTECTED]
To: computer-go@computer-go.org
Sent: Fri, 6 Apr 2007 10:57 AM
Subject: Re: [computer-go] The dominance of search (Suzie v.
Thanks for your report.
The question of UCT versus Alpha-Beta is not open any more in my
opinion. The current state of the art of Monte Carlo tree search is
about 500 Elo points stronger than the version of Crazy Stone you tested
against. Do you believe you can easily catch up with those 500 Elo
I just realized that the UCT describes perfectly the case of trading stocks.
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___
Chrilly wrote:
The main point of my mail was: Search works (at least in 9x9) well. I
think we can agree on this point.
Yes.
For the UCT v. Alpha-Beta question there is a simple proof of the
pudding: Sent us the latest/strongest version and we will try to beat it.
I do not plan to
On 4/6/07, Don Dailey [EMAIL PROTECTED] wrote:
On Fri, 2007-04-06 at 12:43 -0400, [EMAIL PROTECTED] wrote:
Alpha/Beta cutoffs only make sense when calling the evaluation
function twice on the exact same position can be guaranteed to
provide
the exact same value. This is obviously not the
On Fri, 2007-04-06 at 23:41 +0200, Erik van der Werf wrote:
My guess is that the answer which type of search works best for a
given evaluation function depends on the amounts of (deterministic)
bias and (probabilistic) uncertainty in the evaluations (and so far I
see MC mainly as an extremely
I want to clarify however.
If your evaluation function is not deterministic, aspiration
search techniques become very dicey.This is a problem
anyway with hash table implementations and speculate cutoffs
based on the the alpha beta window (and especially the
aspiration window) but it's worth
On 4/6/07, Don Dailey [EMAIL PROTECTED] wrote:
However, there is nothing wrong with using alpha beta
search with an evauation function that is not deterministic.
I agree that some limited amount of non-determinism isn't necessarily
a bad thing, and in some cases it actually helps (e.g., when
(R==1). An incorrect pruning decission is not taken forever. The
general idea is to use information from the search tree to shape the
search tree. Ulf Lorenz from the Univ. Paderborn considers the search
tree as an adaptable error filter.
...
UCT and Monte Carlo. It's not as much Monte
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