Ingo Althoeffer has published some time ago a theoretical article about this
idea. He called it "telescope" evaluation. According his theorectical findings
is the error propagation not better than the usual approach.
K.Chen proposed a similar approach. Use the mean of the last and second-last
evaluation. I tried this. It makes the search more stable. With the normal
search we have strong odd/even effects. At odd depths Suzie evaluates the
position (considerable) higher than at even depths. At odd depths Suzie has 1
move more in the variation than the opponent. This odd/even effect disappears
with the K.Chen mean. Also the search depth increases, because move ordering is
more stable. But the result in the autoplay-matches was worse. One gets effects
like the following: 1 Ply before the horizon the opponent makes a threat. The
evaluation goes down considerable, but there is a defense move and at the
horizon nothing has happened.. But according the mean the programm is still in
some trouble. Or if one reverses the role, Suzie would like to play the
threat-move. This scheme increases therefore even the potential of the programm
to "cheat".
Chrilly
----- Original Message -----
From: [EMAIL PROTECTED]
To: computer-go@computer-go.org
Sent: Monday, April 09, 2007 4:48 PM
Subject: [computer-go] Noise reduction in alpha-beta search
I think following is a way to reduce the noise in alpha-beta search. Instead
of using the evaluation values, use the cummulative evaluation values. That is
the sum of the evaluation values of each node of the playing path under
examination.
Daniel Liu
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