The trend appears to be toward heavier (slower because of more Go
knowledge) playouts in the top programs.


2010/10/31 steve uurtamo <[email protected]>:
> right.
>
> the point is that it is a bit more subtle than the two cases that
> you've described. to answer simply, with the right kind of go
> knowledge, the 10,000 game machine seems, in practice, to be much
> stronger.
>
> the reason is pretty straightforward: a "universal" solver that could
> work for any game without knowledge beyond how to make legal moves
> won't be as strong, for a fixed time limit game, as something that can
> use game knowledge. the extra game knowledge can avoid, on average,
> exploring some silly moves. determining what is silly enough to be
> worth checking for and what isn't is hard, of course.
>
> this can be formalized.
>
> s.
>
> 2010/10/31 Петр Смолов <[email protected]>:
>> I would like to know which program will be stronger: the one with a million 
>> pure random games or the one with 10 000 games, but these games will not be 
>> pure random...
>>
>>
>>> are you asking which converges faster asymptotically?
>>> there is always either a move that creates a winning situation, or no
>>> winning move. for every board position. so trivially the second.
>>> practically, however, i think that a mixture of randomness and smart
>>> go knowledge is what is used in the best programs.
>>> s.
>>> 2010/10/31 Петр Смолов <[email protected]>:
>>> > Hello all!
>>> >
>>> > How do you think what is better: maximim number of simulations (with pure
>>> random games) or much less simulations (but the games are not played 
>>> randomly,
>>> in other words, the program tries to implement a certain move ordering in 
>>> each
>>> game, and of course it takes much more time) ?
>>> > _______________________________________________
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>>> >
>>
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