Author: Vincent Diepeveen
Date: 05:02:40 04/15/02
Go up one level in this thread
On April 15, 2002 at 02:22:07, Tony Werten wrote: >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 evaluating just one half of the evaluation, right or left (or in chess black or white) is already known for half a century. It is not possible in todays programs. If i evaluate half of the position i get for example for white a score of +50 pawns. For black -50 pawns. So you see the problem is simple. Evaluating just a part of the evaluation has been tried from right to left to just doing rude scores or any other part of the evaluation. Giving it a new name and calling it 'features' instead of 'patterns' already says something how little computer game theory they know. >> >>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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