quies could be measured by standard deviation of the scores of the playouts
in strones. If the median score is zero but many games are big wins for
each color then it would seem to indicate a critical quies move somewhere.
I would assume people have experimented with that?
Don
On Mon, Apr 1, 2013 at 4:43 PM, Vincent Zhuang <[email protected]> wrote:
> There is something (that incidentally has been discussed on this mailing
> list before) called quiescence search. Essentially, it redirects playouts
> towards "hot" or "noisy" nodes (based on either the magnitude of the
> noisiness, or if its above a fixed threshold). I'm not aware of an easy way
> to evaluate if a local area is noisy or not, since there appear to be quite
> a few cases.
>
> It seems like quiescence search would be effective in handling small areas
> of (much) higher-than-proportionate significance; however, if I recall
> correctly it has never shown significant results. My memory may be wrong,
> however.
>
>
> On Mon, Apr 1, 2013 at 1:07 PM, Gabriel .Santos
> <[email protected]>wrote:
>
>> Thank you Dave,
>>
>> I will take a look in Winning Ways.
>>
>> ;)
>>
>>
>> On Mon, Apr 1, 2013 at 4:55 PM, <[email protected]> wrote:
>>
>>> Gabriel,
>>>
>>> I don't think that MC players are aware of "local fights". It would be
>>> very nice if a program could divide the board in separate locations,
>>> because the combinatoric explosion would be reduced by a huge factor (4
>>> areas with 16 empty intersections has a much smaller game tree than 1 area
>>> with 64 empty intersections).
>>>
>>> There is a method of combining the results of local endgame fights in a
>>> global result (Winning Ways by Conway, it can be viewed as a way to
>>> determine the optical merging of sepatate game trees), but in earlier
>>> stages of the game it is hard to separate out areas of the board that have
>>> low interaction.
>>>
>>> Perhaps it could be derived in an MC way (statistically) from cross
>>> correlations of board occupance at playout terminal nodes. I gave that a
>>> try a couple of years ago, but I gave up when it didn't seem to give useful
>>> results. Could be due to bugs in my code though.
>>>
>>> Dave
>>> ------------------------------
>>> *Van:* [email protected] namens Gabriel .Santos
>>> *Verzonden:* ma 1-4-2013 19:42
>>> *Aan:* [email protected]
>>> *Onderwerp:* Re: [Computer-go] Weight of moves
>>>
>>> Álvaro,
>>>
>>> When I say "think like a human player ", I mean regarding to the
>>> strategy. For example, when there are several fights
>>> happening simultaneously at the board, a human player can identify them and
>>> decide which one worth more to invest, I thinks this is a really difficult
>>> task in Go. How does he do this judge ? Which features does he analyze?
>>> And there are cases which "try" to mimic the biological solution is worth.
>>> See Neural Networks, Ant Colony Optimization Algorithm, Genetic Algorithm,
>>> etc.
>>>
>>> Santos, Gabriel.
>>>
>>>
>>> On Mon, Apr 1, 2013 at 2:30 PM, Álvaro Begué <[email protected]>wrote:
>>>
>>>>
>>>> On Mon, Apr 1, 2013 at 11:10 AM, Gabriel .Santos <
>>>> [email protected]> wrote:
>>>>
>>>>> I know that it is a lot of questions, but in order to get a computer
>>>>> go machine to outperform a human player I think that the machine should to
>>>>> ratiocinate like a human player.
>>>>
>>>>
>>>>
>>>> Do you also think a machine that carries people very fast should have
>>>> strong legs like a horse? And a machine that can fly should flap its wings
>>>> like a bird? And a closer example: Do you think the same thing about chess
>>>> machines?
>>>>
>>>> In all those cases the engineering solution to the problem was very
>>>> different from the biological solution, and I expect the same will happen
>>>> with computer go. Actually, it's already happening.
>>>>
>>>> Álvaro.
>>>>
>>>>
>>>>
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>>>>
>>>
>>>
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