Relatively speaking chess eval of adding piece values together and
>
> doing nothing else is far closer to optimal evaluation function that
> what is currently available in Go.
>
> Adding piece values together is just a crude starting point in chess.
> It is true that there isn't an obvious candidate in go to play a
> similar role, but it's not like you can get a grandmaster-level
> program by counting beans.
I think there is an obvious candidate - count stones and total
liberties. It's not clear to me that it is worse than just counting
material in chess - because that is a terrible evaluation function in
chess just as counting stones and liberties is terrible in go. Go is
more complex but stone count and territory are the most basic building
blocks of an evaluation function just as head count is in chess.
In chess a good evaluation function has to measure how good each piece
really is. A trapped rook or bad pawn is like a dead group in go. In
a really good go evaluation function you have to measure the probability
of a groups survival and the "goodness" of each stone. In chess you
must do the same.
In go it's more clear cut what the value of a stone is. It's possible
to see that many stones together are "dead." In chess it's rare to see
this except by search. So I don't see any difference - I think these
two respective evaluation functions are roughly equivalent and equally bad.
Of course go is a more complex game and the horizon effect is far more
deadly in GO than in Chess so this evaluation produces an even weaker Go
program than in Chess. I'm not sure what is meant by "farther from
optimal" because the tree search is far more complex and horizon effects
are greater. But chess can have pretty wicked horizon effects too
and thus go presents more difficult version of the same problems - not
different problems. (Yes, the solution in chess is often to ignore
problems - but that is not the same as saying they are not problems.)
- Don
>
> Álvaro.
>
>
>
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