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Re: [computer-go] An [open] question on game tree search theory



Erik van der Werf wrote:


What is a probability of winning?

Or probability that the game tree node is a game-theoretic win? What
does this mean? One game node is either win or not, so there is no
stochastic experiment. You need to have a repeated experiment or a
population larger than one object to be able to speak of probabilities.

You have this larger population because your evaluation function has to generalize to a huge number of positions. With 'real world' limited computing power you will not be able to store all possible positions independently or describe them perfectly by some limited number of parameters. Consequently there will be multiple positions that (implicitly) map to the same (or highly similar) internal state, thus providing a basis for statistics.
This is true... so to rephrase, if your evaluation function maps every position p to, say, an integer from 0 to 100 ("the internal state"), you could, in theory, compute for every class (0..100) the average fraction of game-theoretic wins in the particular class. This would make sense.

However, this is maybe impossible to implement in practice, because the game-theoretic values are not available in the first place... is there any pragmatic way to bring this concept into use? Human player could provide pseudo-game-theoretic values for sample positions?

I see a difference with auto-tuning evaluation functions; auto-tuning evaluation functions modify the evaluation function weights and structures, here the evaluation function is fixed and we are just doing statistical analysis... but maybe these two approaches are the same behing the scenes.

--
Antti Huima (Mr.)
Director, Conformiq Tools
mobile: +358 40 528 8667
email: antti.huima@xxxxxxxxxxxxxxxxx

Conformiq Software Ltd.
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FIN-02600 Espoo, Finland
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