Author: Alessandro Damiani
Date: 10:37:19 04/14/02
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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. 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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