Author: Uri Blass
Date: 16:50:42 10/22/00
Go up one level in this thread
On October 22, 2000 at 18:04:35, Thorsten Czub wrote:
<snipped>
>ok - IMO what christophe has done is THE FIRST STEP.
>He had made tiger into a gambit-tiger who is not longer
>searching for SOMETHING in the tree, hoping to find anything,
>but to direct the program into chaos, into action, into
>the mirror-world where positions are not EXACT and also
>it does not interest how the score is, but it interest
>what you can make out of the position: i mean: the chances
>of a position.
I do not believe it
GambitTiger like other programs plays the move with the highest score.
There are sometimes big positional scores for king attack when other programs do
not have the big positional scores and are based mainly on material scores but
it does not change the fact that gambit plays the move that gives the highest
score from gambit's point of view.
<snipped>
>For a classical program, to keep the search fast, the evaluation at each
>node must, of necessity, be brief. This evaluation is usually no more
>than a weighting given for each piece on each square (for example a
>knight might be worth 3.3 pawns on centre squares and 2.9 pawns on edge
>squares) and evaluation of the pawn structure for doubled pawns, passed
>pawns etc.. The classical pre-processing function looks for themes in
>the position and adjusts the square weightings accordingly - for
>example, if a knight is attacking a square next to the king, then
>increase the weighting for all the squares that the queen could
>cooperate with the knight in making a king attack, increase the knight
>weighting to keep it on the original square, increase other cooperating
>piece weightings and so on. There is no doubt that this approach works
>but it cannot be the way forward. Pre-process ing knowledge becomes more
>stupid with increasing search depth, as positions deep in the search
>tree becomes more removed from the assumptions of the original position,
>the square weighting adjustments become more irrelevant (why weight the
>squares for the queen after the cooperating knight has been removed from
>the board ?- but the classical paradigm doesn't understand that !). I
>call this type of search Artificial Stupidity (AS). Since all the
>current programs operate in this way, ELO grading lists and inter-program
>tournaments are no more than a reflection of the partially-sighted
>playing the blind, whose AS algorithm is most efficient, but it is not
>chess.
I know that a lot of chess programs do not operate in this way.
Chris says that programs use only preprocessor and piece square tables+pawn
structure evaluation.
It is not close to be right.
<snipped>
>Dynamic knowledge v. Combinational knowledge
>============================================
>
>Oxford Softworks CCS2-v9.0
>White: CCS2 486/33
>Black: Genius2 486/33
>Venue: 1 minute per move
>Comment: 1-0
>
>1. e4 e6
>2. d4 d5 1
>3. Nc3 Nf6 3
>4. Bg5 Be7 5
>5. e5 Nfd7 8
>6. h4 Bxg5
>7. hxg5 { CCS2's opening book ends }
> .... Qxg5
>8. Nf3 Qd8 { Genius2's opening book ends }
>9. Bd3 h6
>10. Qd2 { CCS2's dynamic knowledge - preventing O-O because
> of the threat of Rxh6 }
> .... c5
>11. Nb5 O-O { Catastrophic - any reasonable club player can
> see this move is a disaster, but Genius2 has no
> dynamic knowledge, there is no immediate mate so Genius2
> thinks all is ok ! }
The position after Nb5 is a good test position
The target is to avoid 0-0
[D]rnbqk2r/pp1n1pp1/4p2p/1NppP3/3P4/3B1N2/PPPQ1PP1/R3K2R b KQkq - 0 1
Some programs like Crafty may need some minutes but I believe that on fast
hardware all the top programs have no problem to avoid 0-0.
Part of them like Fritz5.32 never consider 0-0 as best.
Uri
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