Author: Alessandro Damiani
Date: 13:24:44 04/14/02
Go up one level in this thread
On April 14, 2002 at 13:57:38, Vincent Diepeveen wrote:
>On April 14, 2002 at 13:37:19, Alessandro Damiani wrote:
>
>>Hi Vincent,
>>
>>You too much concentrated on the game Mawari. I think they choose Mawari to have
>>a simple framework to experiment with. I guess a Mawari engine is far simpler
>>than a Chess one. So, forget Mawari. :)
>>
>>You are right, alpha-beta evaluation functions are like lazy evaluation. But,
>>the big difference is that an alpha-beta evaluation function is an algorithm
>>that traverses a classification tree. I have in mind the picture of an ordered
>>hierarchical structure of position features (a tree of features). At first sight
>>it seemed to me like that (right, I didn't take the time to read the whole text
>>:).
>>
>>We both agree on the bad effect of lazy evaluation on positional play, but an
>>alpha-beta evaluation function seems to be different: the bounds on a feature's
>>value range are not estimated.
>>
>>But maybe I am wrong.
>
>Yes you are wrong.
>
>Let them show pseudo code.
>
>Then you see what they describe is 100% the same and completely
>not working.
>
>What they do is kind of:
>
> "we have a great new feature and we call it X"
>
>In reality they invented old feature F which was already proven
>to be not working.
>
>So in fact 2 mistakes are made by them
> a) not checking out existing literature and experiments done
> b) committing scientific fraude by describing something
> existing.
>
I understand what you mean, but it is better if you first have got more
information before you judge. Here is the pseudo code taken from the text:
function static_evaluation(p: position;
alpha, beta: real;
k: evaluation node): real;
begin
for i:= 1 to D do unknown[i]:= true;
while true do begin
if k.beta <= alpha then return alpha;
if k.alpha >= beta then return beta;
if leaf(k) then return k.alpha;
if unknown[k.feature] then begin
vector[k.feature]:= get_feature(p, k.feature);
unknown[k.feature]:= false
end;
if vector[k.feature] <= k.split_value then
k:= k.left
else
k:= k.right
end
end
where D is the number of features in a position.
Here is the link where I took the text from:
http://satirist.org/learn-game/lists/papers.html
Look for "Bootstrap learning of alpha-beta-evaluation functions (1993, 5
pages)".
Alessandro
>>
>>
>>On April 14, 2002 at 11:42:34, Vincent Diepeveen wrote:
>>
>>>On April 14, 2002 at 04:26:52, Alessandro Damiani wrote:
>>>
>>>Seems to me that these idiots never have figured out what already
>>>has been tried in computerchess world.
>>>
>>>Of course i'm not using their 'concept' which already exists
>>>by the way. These guys are beginners everywhere of course.
>>>Mawari, every idiot who programs for that game can get
>>>world champ there of course, or pay levy to get a gold medal...
>>>...if i may ask...
>>>
>>>What works for a 2000 rated chessprogram to experiment
>>>with doesn't work for todays strong chessprograms simply.
>>>Mawari programs when compared to chess programs are at 2000
>>>level of course, relatively seen to how much time and
>>>effort has been invested in mawari programs.
>>>
>>>If i read their abstract well then in fact they define a
>>>'partial' evaluation, already known under the name
>>>lazy evaluation using a quick evaluation.
>>>
>>>That's a complete nonsense approach. It's pretty much the same like
>>>lazy evaluation based upon a quick evaluation, it's most likely
>>>exactly the same, if not 100% similar.
>>>
>>>If i would describe here how much time i invested in making
>>>a quick evaluation which evaluates some rude scores, and which
>>>with some tuning when to use it and when to not use it, that
>>>it always scores when used within 3 pawns in 99% of the positions,
>>>then people would not get happy.
>>>
>>>I invested *loads* of time there in the past.
>>>
>>>More important, i generated big testcomparisions here to see
>>>when the quick eval worked and when not. That's why i could
>>>conclude it didn't work.
>>>
>>>Even more unhappy i was when i tested with this concept. Disaster.
>>>Yes it was faster concept, but here the amazing results
>>> - positional weaker
>>> - tactical weaker
>>>
>>>the first i wasn't amazed about of course, but the second i was.
>>>i was pretty amazed to find out that these 1% of the evaluations
>>>where the quick evaluation gave a score but evaluated it wrong,
>>>really amazing that these evaluations cause a tactical way better
>>>engine.
>>>
>>>Simply majority of tactical testset positions get solved by evaluation
>>>and NOT by seeing a bit more tactics.
>>>
>>>In short it's not working simply to use a lazy evaluation in a program with
>>>a good evaluation which also has high scores for things like king
>>>safety.
>>>
>>>>Hi all,
>>>>
>>>>I am wondering if someone uses "alpha-beta-Evaluation Functions" by Alois P.
>>>>Heinz and Christophe Hense. Below is the abstract of the text.
>>>>
>>>>Alessandro
>>>>
>>>>
>>>>Bootstrap Learning of alpha-beta-Evaluation Functions
>>>>Alois P. Heinz Christoph Hense
>>>>Institut für Informatik, Universität Freiburg, 79104 Freiburg, Germany
>>>>heinz@informatik.unifreiburg.de
>>>>
>>>>Abstract
>>>>We propose alpha-beta-evaluation functions that can be used
>>>>in gameplaying programs as a substitute for the traditional
>>>>static evaluation functions without loss of functionality.
>>>>The main advantage of an alpha-beta-evaluation function is that
>>>>it can be implemented with a much lower time complexity
>>>>than the traditional counterpart and so provides a signifi
>>>>cant speedup for the evaluation of any game position which
>>>>eventually results in better play. We describe an implemen
>>>>tation of the alpha-beta-evaluation function using a modification
>>>>of the classical classification and regression trees and show
>>>>that a typical call to this function involves the computation
>>>>of only a small subset of all features that may be used to
>>>>describe a game position. We show that an iterative boot
>>>>strap process can be used to learn alpha-beta-evaluation functions
>>>>efficiently and describe some of the experience we made
>>>>with this new approach applied to a game called malawi.
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