Author: Tony Werten
Date: 23:22:07 04/14/02
Go up one level in this thread
On April 14, 2002 at 16:24:44, Alessandro Damiani wrote:
>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.
>>
Impressive. Here is the pseudo code for the skip-search heuristic.
function perfect_evaluation(p:position):value:int
begin
for i:=1 to all_possible_features(p) do
begin
add(value,score_of(i);
end;
return(value);
end;
( I have some pseudo-code for the meaning of life as well)
Tony
>
>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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