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
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Re: [Computer-go] re comments on Life and Death

[David Fotland]

Some details at http://www.smart-games.com/knowpap.txt

Completely agree that connections and group strength estimates are key to 
strength, and are very difficult to get right.  There are many tricky cases.  
For connections I used shapes and local tactics to determine connectivity and 
threat points, and handled some cases of adjacent connections with shared 
threats.  For group strength I had about 20 classes with separate evaluators 
(two clear eyes, one big eyes, seki, semeai, run-or-live, 
one-eye-ko-threat-to-live, dead-if-move-first, etc, etc).

Connection status was used to collect stones into groups.  Groups strength was 
the core concept feeding into the full board evaluation, which tried to 
estimate the score.

David

> -Original Message-
> From: Computer-go [mailto:computer-go-boun...@computer-go.org] On
> Behalf Of Robert Jasiek
> Sent: Friday, September 04, 2015 12:34 AM
> To: computer-go@computer-go.org
> Subject: Re: [Computer-go] re comments on Life and Death
> 
> On 04.09.2015 07:25, David Fotland wrote:
> > group strength and connection information
> 
> For this to work, group strength and connection status must be a)
> assessed meaningfully and b) applied meaningfully within a broader
> conceptual framework. What were your definitions for group strength
> and connection status, for what purposes did you use them and how did
> you apply them?
> 
> --
> robert jasiek
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Re: [Computer-go] re comments on Life and Death

[David Fotland]

I forgot, I did publish a paper on Many Faces: 
https://www.researchgate.net/publication/220174515_Static_Eye_Analysis_in_The_Many_Faces_of_Go

I'm not sure it's available online.

David

> -Original Message-
> From: Computer-go [mailto:computer-go-boun...@computer-go.org] On
> Behalf Of Robert Jasiek
> Sent: Friday, September 04, 2015 10:29 AM
> To: computer-go@computer-go.org
> Subject: Re: [Computer-go] re comments on Life and Death
> 
> On 04.09.2015 17:55, Stefan Kaitschick wrote:
> >It is just too far removed from MC concepts to be productively
> >integrated into an MC system. And no matter what, MC has to be the
> >starting  point
> 
> No. It is also possible to construct it the other way round. Start
> with an expert system. Whenever that needs some "calculation" and
> basic counting or limited reading fail, MC can come in to do the
> calculation.
> E.g., an expert system can identify groups of likely connected
> strings, then MC can calculate if indeed (statistically) the
> connection is given.
> 
> --
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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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Re: [Computer-go] re comments on Life and Death

[Robert Jasiek]

On 05.09.2015 08:00, David Fotland wrote:

Completely agree that connections and group strength estimates are key to 
strength, and are very difficult to get right.


From the POV of humans, I have described connection meaningfully. The 
remaining problem is the variety of application in principles and higher 
concepts.


Whether group strength is needed at all depends very much on what you 
mean by it.



For connections I used shapes and local tactics


Shapes are not needed, unless you want to use them to prune tactics. 
However, if the tactics verification is slow for standard shapes, I'd 
say this is its fault.


> Connection status was used to collect stones into groups.

Fine, provided this is not a static partition. Other considerations 
(such as sacrifice) can make it necessary to alter groups dynamically.



For group strength I had about 20 classes with separate evaluators
(two clear eyes, one big eyes, seki, semeai, run-or-live, 
one-eye-ko-threat-to-live, dead-if-move-first, etc, etc).


Was group strength an object of several parameters or was it a single 
number derived from all those parameters? IMO, a single number cannot be 
meaningful in general.



Groups strength was the core concept feeding into the full board evaluation, 
which tried to estimate the score.


But what WAS your group strength...?:)

Score estimation of a given position should also depend on territory 
counts, not only on group strength etc.


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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
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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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[Computer-go] open bank

[djhbrown .]

chitchat satisfies the human desire for self-expression and/or self
aggrandisement, but it usually doesn't much help the enquiring student​ of
science, unless you're chatting with Minsky or Chomsky.

there are 1001 publishers around, all of whom demand money for
information.  and one or two that don't, as they make their revenues from
bundling advertisers' spam into whatever they give you for free.

it is tedious to have to trawl through kilograms of garbage, only to find
that the diamond you are looking for is blocked by a guard demanding money
or signup for more circular spam.

there are a few sources of open actual information, and authors sometimes
choose one or another as their vehicle.

YouTube does it for movies, would someone like to do it for papers about Go
programming?  It could simply be a list of URLs that contain information
instead of the usual metainformation.

Here's a start:

1999 Static analysis of life and death in the game of Go. Ken Chen, Zhixing
Chen. https://webdocs.cs.ualberta.ca/~games/go/seminar/2002/020703/ld.pdf

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[Computer-go] re Static analysis of life and death in the game of Go. Ken Chen, Zhixing Chen.

[djhbrown .]

it feels unintuitive to map the poetic notion of half an eye onto the
arithmetical value 1/2.  half eyes are only useful if each side of the half
eye also has a whole eye, so instead of thinking about half-eyes and doing
sums with them, a computer could have the concepts of "shared eye" and
"false eye".  or am i missing something?

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