Author: Ratko V Tomic
Date: 21:53:58 10/16/00
Go up one level in this thread
> The only thing I was pointing out is that if a program evaluates > something as +3, when material is even, then it will evaluate that > same position as even, when it is a piece down. That doesn't mean at all it would give a piece in that position. Only a simple linear/additive form of the evaluation function would have such side-effect. But a nonlinear combination of separate evaluation components (as exemplified by Samuels' checkers program) need not have such drawback. While Gambit Tiger might not go as far to use as general mapping as Samuels program did (since the chess parameter space is vastly larger, in extent & number of dimensions, than that of checkers, and would thus take too long to tune), there are great many intermediate levels of non-linearity, some of which might work better and be practically tunable in chess. For example, the sum of squares of properly chosen term deviations would give much greater weight to the term which stands out in a given position, producing an effect of program concentrating on that aspect/feature of the position. That is in fact much more humanlike way of analyzing a position. A good player knows what is the most relevant in a position and spends his computational resources on the relevant aspects, ignoring the irrelevant ones. The Botvinnik's scheme takes this concept (of over-emphasis of relevant) further by explicitly guiding multiple alpha-beta searches from the same position, but using different objectives (and evaluation functions) each time. In that case you would see great jumps in evaluation, which merely reflects different perspectives one might take in a given position. It is natural that if you analyze a position looking for, say, a king attack, then analyze the same position looking for a better pawn structure for endgame, you will have widely varying estimates, e.g. if king attack doesn't seem useful, you may get a very low score in that search (due to weakening/sacrifices needed to get the attack off the ground). At the same time the pawn structure oriented search may give an even position. On the other hand, if the king attack search looks promissing, triggering many criteria for a strong (but beyond horizon) attack, you may get a high evaluation, well beyond the material balance on the board. For example you could get +3 value, but that doesn't mean you would get 0 value if you take a piece off the board (e.g. that piece is contributing to the king attack or defending against the break on the other side etc). Without a piece, there would likely not be any triggering of the high score for the king attack, any you might simply get -3 score. Taking one more step in abstracting the meaning of such high evaluation swings (the first two levels being nonlinearity and different perspectives), one can view the large score (well beyond the simple additive variation) as a reflection of a much longer term estimate. This longer term estimate comes from specialized evaluations tuned for a given type of position, which may not be available or usable in all positions. But when it is judged usable by the program, it will give a value much closer to what the regular evaluation may see 20 or 30 plies later. E.g. in getting ready for a king side attack, no checkmate or large material gain is seen in the nodes examined, but the specialized long term estimators indicate that in such position the expected gain may be a piece by the time attack is completed, some 20-30 plies later. The large score variation is thus a kind of far extrapolation reaching much closer to the final game values (which could be, say, +99 for a win) than what regular evaluation offers. Obviously, the trick is to come up with such long term evaluations. But, observing human play, we know these do exist in some implicit form in the mind of strong players. So, since they do exist, it isn't beyond possible that someone (Christophe T., Chris W., David K., Marty H., ...) may have teased out some of that knowledge.
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