Re: [Computer-go] Maximum Frequency method

[Hideki Kato]

uurtamo .: :
>BTW: have you tried other distributional difference metrics, or does K-L
>have properties that you like?

No. #I haven't tried any distributional difference. The paper is not 
mine.

As MC simulations add big randomness to the scores, I wonder if 
maximizing frequency method is better than simple average.  My 
expectation is it's almost the same.  Improving simulations has much 
more priority for me.

#Mathematically, K-L divergence would be the best measure. anyway.  
Problem is maximizing frequency may not approximate maximizing the 
divergence due to the simulations' random biases.

Hideki

>Thanks,
>
>steve
>On Sep 5, 2015 1:35 AM, "Hideki Kato"  wrote:
>
>> djhbrown .: <
>> capsify9fub60pd3lzdyhdpupffgyenv4t+m47okwphzrb4q...@mail.gmail.com>:
>> >thank you for sharing the paper.
>> >
>> >"the Maximum Frequency method is based on the
>> >maximization of the difference between the expected reward of
>> >the optimal move and that of others"
>> >
>> >intuitively it feels that biasing random search towards the optimal route
>> >would yield reduced failure rates, yet it does seem to depend on knowing
>> >what the optimal route is beforehand.
>>
>> UCT is never a random search but deterministic.
>>
>> Maxmizing KL-divergence just speed-up the convergence of the interative
>> algorithm.
>>
>> Hideki
>>
>> >if i knew the optimal route to get from A to B, i wouldn't bother doing a
>> >random search, but just follow it.
>> >
>> >"This property [“bias in suboptimal moves”] means that the impact of
>> >missing the optimal move is much greater for one player than it is for the
>> >opponent."
>> >
>> >i find this conclusion puzzling because Go is a zero-sum game, so what is
>> >good for one side is equally bad for the other, not variably so.  I have
>> >not checked the statistical inference calculations to see whether there is
>> >an error in them.
>> > inline file
>> >___
>>
>> >Computer-go mailing list
>>
>> >Computer-go@computer-go.org
>>
>> >http://computer-go.org/mailman/listinfo/computer-go
>> --
>> Hideki Kato 
>> ___
>> Computer-go mailing list
>> Computer-go@computer-go.org
>> http://computer-go.org/mailman/listinfo/computer-go
> inline file
>___

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Hideki Kato 
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Re: [Computer-go] Maximum Frequency method

[uurtamo .]

BTW: have you tried other distributional difference metrics, or does K-L
have properties that you like?

Thanks,

steve
On Sep 5, 2015 1:35 AM, "Hideki Kato"  wrote:

> djhbrown .: <
> capsify9fub60pd3lzdyhdpupffgyenv4t+m47okwphzrb4q...@mail.gmail.com>:
> >thank you for sharing the paper.
> >
> >"the Maximum Frequency method is based on the
> >maximization of the difference between the expected reward of
> >the optimal move and that of others"
> >
> >intuitively it feels that biasing random search towards the optimal route
> >would yield reduced failure rates, yet it does seem to depend on knowing
> >what the optimal route is beforehand.
>
> UCT is never a random search but deterministic.
>
> Maxmizing KL-divergence just speed-up the convergence of the interative
> algorithm.
>
> Hideki
>
> >if i knew the optimal route to get from A to B, i wouldn't bother doing a
> >random search, but just follow it.
> >
> >"This property [“bias in suboptimal moves”] means that the impact of
> >missing the optimal move is much greater for one player than it is for the
> >opponent."
> >
> >i find this conclusion puzzling because Go is a zero-sum game, so what is
> >good for one side is equally bad for the other, not variably so.  I have
> >not checked the statistical inference calculations to see whether there is
> >an error in them.
> > inline file
> >___
>
> >Computer-go mailing list
>
> >Computer-go@computer-go.org
>
> >http://computer-go.org/mailman/listinfo/computer-go
> --
> Hideki Kato 
> ___
> Computer-go mailing list
> Computer-go@computer-go.org
> http://computer-go.org/mailman/listinfo/computer-go
___
Computer-go mailing list
Computer-go@computer-go.org
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Re: [Computer-go] Maximum Frequency method

[Hideki Kato]

djhbrown .: 
:
>thank you for sharing the paper.
>
>"the Maximum Frequency method is based on the
>maximization of the difference between the expected reward of
>the optimal move and that of others"
>
>intuitively it feels that biasing random search towards the optimal route
>would yield reduced failure rates, yet it does seem to depend on knowing
>what the optimal route is beforehand.

UCT is never a random search but deterministic.

Maxmizing KL-divergence just speed-up the convergence of the interative 
algorithm.

Hideki

>if i knew the optimal route to get from A to B, i wouldn't bother doing a
>random search, but just follow it.
>
>"This property [“bias in suboptimal moves”] means that the impact of
>missing the optimal move is much greater for one player than it is for the
>opponent."
>
>i find this conclusion puzzling because Go is a zero-sum game, so what is
>good for one side is equally bad for the other, not variably so.  I have
>not checked the statistical inference calculations to see whether there is
>an error in them.
> inline file
>___

>Computer-go mailing list

>Computer-go@computer-go.org

>http://computer-go.org/mailman/listinfo/computer-go
-- 
Hideki Kato 
___
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[Computer-go] Maximum Frequency method

[djhbrown .]

thank you for sharing the paper.

"the Maximum Frequency method is based on the
maximization of the difference between the expected reward of
the optimal move and that of others"

intuitively it feels that biasing random search towards the optimal route
would yield reduced failure rates, yet it does seem to depend on knowing
what the optimal route is beforehand.

if i knew the optimal route to get from A to B, i wouldn't bother doing a
random search, but just follow it.

"This property [“bias in suboptimal moves”] means that the impact of
missing the optimal move is much greater for one player than it is for the
opponent."

i find this conclusion puzzling because Go is a zero-sum game, so what is
good for one side is equally bad for the other, not variably so.  I have
not checked the statistical inference calculations to see whether there is
an error in them.
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