Hi!
On Wed, Jan 20, 2010 at 09:39:31PM +0000, Jacques Basaldúa wrote:
> > As explained by others a "strong SE for ALL positions" is equivalent
> > to a strong program.
>
> This is only true if you replace the word “strong” by “perfect”. It is
> trivial, that a perfect evaluation function gives a perfect player using
> a 1-ply search, but that is not true for strong evaluation functions.
> Converting a strong evaluation function into a strong program is still
> a hard problem. “Almost” good moves are frequently worse than random
> moves. (E.g. a move that “almost saves” a group that cannot be saved
> or “almost kills” a group that cannot be killed gives one prisoner to
> the opponent and wastes one turn. This is worse than pass while random
> moves are with a high probability better than pass.)
This seems like not very productive line of argumentation unless
preceded with more exact definitions of strong.
> 2. Now my proposal: An MC based SE (Score Estimator).
>
> Use a strong MCTS program unmodified, except in a small detail to collect
> data about by how many points each simulation is won.
..snip..
Actually, there is a thread about exactly this on fuego-devel going on
right now; someone wants to use `final_status_list dead` in the middle
of game and is surprised about bogus results from Fuego. As has been
noted by Martin Mueller:
> This said, note that the main strength of Monte-Carlo searches comes not
> from accurate scoring, but from comparing the relative success of moves.
> E.g. if there is a systematic bias in the evaluation, the scores can be
> quite terrible, but often the program can still produce a reasonable
> move, since most scores are off by roughly the same amount.
Jacques wrote:
> After all, it is easier to get a SE from a strong program than the other
> way round ;-)
That's not entirely clear. ;-)
--
Petr "Pasky" Baudis
A lot of people have my books on their bookshelves.
That's the problem, they need to read them. -- Don Knuth
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