Author: Komputer Korner
Date: 16:17:26 03/01/98
Go up one level in this thread
The problem is that all strategy (which the evaluation really is) breaks down to extremely long tactics. Chess is just one long path puzzle. No amount of evaluation (strategy) tuning will ever make up for seeing 50 moves ahead. There are exceptions to almost every strategical rule or maxim. Fact of Chess. This doesn't stop computer programmers from trying to get their program's strategy to the level of a Kasparov, but don't forget that Kasparov's understanding was obtained from thousands of games and guided by world champions ( Bottvinnik..etc) so that he knows what plans are best in certain positions. So maybe evaluation functions must be stored like the opening books. Each type of opening would have its own evaluation function. A lot of work, but how much work has a player like Kasparov put into chess in a lifetime? On February 25, 1998 at 18:09:58, Amir Ban wrote: >On February 25, 1998 at 16:54:55, Jay Scott wrote: > >> >>On February 25, 1998 at 05:09:41, Amir Ban wrote: >> >>>We all know what the BEST evaluation is. It's the one coming out of >>>perfect knowledge of the game. But what is good evaluation ? More >>>precisely, given two evaluation functions, how do you decide which is >>>better ? >> >>The question "which one is better?" is meaningless by itself. Better >>for what? >> > >Better for Computer Chess. Since this is the name of the newsgroup, I >thought I don't need to say that. > > >>Label the set of all chess positions with the positions' game- >>theoretic values, win=1 loss=0.5 draw=0. One thing you might like >>your evaluation function to do is minimize, say, mean squared >>error. You might like a good statistical fit to the truth, in >>other words. >> >>But it's easy to construct an evaluation which has an excellent >>statistical fit yet plays bad chess. For example, imagine an >>evaluator which is perfect except that it thinks that white is >>winning after 1. g4 e5 2. f4. The mean squared error is >>negligible, since only a few positions are wrong, but a program >>that relies on it is going to lose a lot of Fool's Mates. >> >>A program could also play perfectly with an evaluation which >>had a poor statistical fit. All that's necessary is for one >>of the optimal moves to be evaluated highest in any position >>that the program can reach with optimal play up to that point. >> > >Right. I'm not so much interested in ideas that are dismissed in the >next sentence. I am looking for a formulation which at least someone >considers to be correct. The fact that it's not easy to find one is >surprising in itself. It shows that we don't have a good idea of what >what we are doing when we are busy "improving" our evaluation". > >Amir
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