Author: Vincent Diepeveen
Date: 10:57:38 04/14/02
Go up one level in this thread
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.
>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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