Re: [Computer-go] Computer Go tactics and matrix completion
To me (a weak Go player), the priorities on parameter changes from Fuseki,
to when counting score on the board becomes relatively accurate, to Yose /
end-game. So I've been using different parameters for different stages in
a game for my non-MCTS non-DCNN traditional-logic engine for a long time,
and it is obvious to me that extending this concept to the differences in
play style or strength/weakness of opponents derived from game records
would have benefits.
Old GNUGo had in-game parameter tuning methods already implemented on
Territorial orientation, Attacking orientation, Invasion orientation, etc.
But of course I do realize 'parameter' can be a bit different from my
example to a modern normal bot. I may think about using Matrix Completion
in the future on CGOS opponents, which might help how my bot will do in the
UEC Cup if it is allowed in the rules. After all, "whom they are playing
against" is an important information a Pro processes before and during a
game.
Tokumoto
On Sat, Jan 16, 2016 at 4:19 AM, Gonçalo Mendes Ferreira
wrote:
> I haven't read how programs 'catalog' play under various categories, like
> for http://gostyle.j2m.cz/
> but only if the acquisition of some features is easier than others, giving
> an incomplete view of the
> opponents, would a technique like matrix completion be necessary, no?
>
> If from just 2-5 games we can catalog a player sufficiently well, any
> algorithm for parameter tuning
> should suffice for generating a probability distribution of the
> application of different parameter
> vectors. Can it be done with just a few games? Probably. Matches are long.
> Probably even changing
> parameters automatically midgame would also be possible and be more
> powerful for dealing with
> pesky versatile players.
>
> I guess what I'm saying is that to me it is seems overkill for Go (besides
> the whole remembering
> every player in KGS thing).
>
> Gonçalo
>
>
> On 15/01/2016 14:53, "Ingo Althöfer" wrote:
>
>> Hello,
>>
>> the topic "matrix completion" has become famous, in
>> particular after a competition organized by the
>> NetFlix company. NetFlix has many customers and (not so)
>> many films. They want to generate personalized
>> recommendations for their customers ("you might also like
>> movie X").
>>
>> Transfer to the world of go on internet servers:
>> One problem of go bots against human go players is that
>> humans learn quickly about special weaknesses of the bot.
>> Now assume some fictive go bot "Bimbo" with several
>> secrete parameter settings (S_1, ..., S_m). When a
>> human plays on KGS against Bimbo he does not know which
>> parameter set is just activated. The team behind Bimbo
>> is switching between parameter sets, either randomly or
>> by some tactics.
>>
>> What the Bimbo team knows is how well which humans handled
>> which sets in the past. What they want to find is the
>> "best" parameter set for the current opponent. Here "best"
>> is meant with respect to the bot chances. Matrix completion
>> may help to find "appropriate" settings.
>>
>> Observe that NetFlix is dealing with thousands of films
>> and millions of customers. In contrast, on KGS you have
>> perhaps a few hundred opponents and about a dozen or so
>> parameter sets. So, "matrix completion" in the Bimbo team
>> would not be a very hard task.
>>
>> Ingo.
>>
>> Wikipedia-Link:
>> https://en.wikipedia.org/wiki/Matrix_completion
>>
>> PS. In computer chess, switching secretly between several
>> program versions was sometimes called "engine revolver".
>>
>
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Re: [Computer-go] Computer Go tactics and matrix completion
I haven't read how programs 'catalog' play under various categories,
like for http://gostyle.j2m.cz/
but only if the acquisition of some features is easier than others,
giving an incomplete view of the
opponents, would a technique like matrix completion be necessary, no?
If from just 2-5 games we can catalog a player sufficiently well, any
algorithm for parameter tuning
should suffice for generating a probability distribution of the
application of different parameter
vectors. Can it be done with just a few games? Probably. Matches are
long. Probably even changing
parameters automatically midgame would also be possible and be more
powerful for dealing with
pesky versatile players.
I guess what I'm saying is that to me it is seems overkill for Go
(besides the whole remembering
every player in KGS thing).
Gonçalo
On 15/01/2016 14:53, "Ingo Althöfer" wrote:
Hello,
the topic "matrix completion" has become famous, in
particular after a competition organized by the
NetFlix company. NetFlix has many customers and (not so)
many films. They want to generate personalized
recommendations for their customers ("you might also like
movie X").
Transfer to the world of go on internet servers:
One problem of go bots against human go players is that
humans learn quickly about special weaknesses of the bot.
Now assume some fictive go bot "Bimbo" with several
secrete parameter settings (S_1, ..., S_m). When a
human plays on KGS against Bimbo he does not know which
parameter set is just activated. The team behind Bimbo
is switching between parameter sets, either randomly or
by some tactics.
What the Bimbo team knows is how well which humans handled
which sets in the past. What they want to find is the
"best" parameter set for the current opponent. Here "best"
is meant with respect to the bot chances. Matrix completion
may help to find "appropriate" settings.
Observe that NetFlix is dealing with thousands of films
and millions of customers. In contrast, on KGS you have
perhaps a few hundred opponents and about a dozen or so
parameter sets. So, "matrix completion" in the Bimbo team
would not be a very hard task.
Ingo.
Wikipedia-Link:
https://en.wikipedia.org/wiki/Matrix_completion
PS. In computer chess, switching secretly between several
program versions was sometimes called "engine revolver".
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[Computer-go] Computer Go tactics and matrix completion
Hello,
the topic "matrix completion" has become famous, in
particular after a competition organized by the
NetFlix company. NetFlix has many customers and (not so)
many films. They want to generate personalized
recommendations for their customers ("you might also like
movie X").
Transfer to the world of go on internet servers:
One problem of go bots against human go players is that
humans learn quickly about special weaknesses of the bot.
Now assume some fictive go bot "Bimbo" with several
secrete parameter settings (S_1, ..., S_m). When a
human plays on KGS against Bimbo he does not know which
parameter set is just activated. The team behind Bimbo
is switching between parameter sets, either randomly or
by some tactics.
What the Bimbo team knows is how well which humans handled
which sets in the past. What they want to find is the
"best" parameter set for the current opponent. Here "best"
is meant with respect to the bot chances. Matrix completion
may help to find "appropriate" settings.
Observe that NetFlix is dealing with thousands of films
and millions of customers. In contrast, on KGS you have
perhaps a few hundred opponents and about a dozen or so
parameter sets. So, "matrix completion" in the Bimbo team
would not be a very hard task.
Ingo.
Wikipedia-Link:
https://en.wikipedia.org/wiki/Matrix_completion
PS. In computer chess, switching secretly between several
program versions was sometimes called "engine revolver".
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