Hi Álvaro,

I've done things like that, except I didn't use games by strong
computer opponents (none existed at the time), so just human amateur
games. In my experience the critical part is in learning about life &
death. Once you have that, estimating ownership is fairly easy,
asymptotically reaching near 100% prediction accuracy toward the end
of the game. See the following papers for more details:

http://erikvanderwerf.tengen.nl/pubdown/learning_to_score_extended.pdf
http://erikvanderwerf.tengen.nl/pubdown/learningLifeandDeath.pdf
http://erikvanderwerf.tengen.nl/pubdown/predicting_territory.pdf

or just read the second half of my PhD thesis :)

http://erikvanderwerf.tengen.nl/pubdown/thesis_erikvanderwerf.pdf


BTW it's great to see this list come back from the dead. Thanks Petr & Michael!

Best,
Erik


On Sat, Dec 20, 2014 at 4:43 PM, Álvaro Begué <alvaro.be...@gmail.com> wrote:
>
> Hi,
>
> There are things a CNN could probably do well, if only we had the right
> database to train it. I have in mind these two possibilities:
>  * using a CNN as an evaluation function,
>  * using a CNN to estimate "ownership" for each point (i.e., a number
> between -1 and 1 that is an estimate of who is going to end up scoring it).
>
> So we need a large set of positions labelled with a final score for the game
> and who ended up scoring each point.
>
> I believe the right database to use for this purpose would consist of
> positions from games played by strong computer opponents which play to
> maximize score and which play to the bitter end, passing only when the
> opponent has no dead stones left on the board.
>
> I would like to know if you think this would be an interesting resource to
> have, if you have any recommendations on what engine(s) to use and if you
> would be willing to collaborate in creating it. Any other comments are
> welcome too, of course.
>
> Cheers,
> Álvaro.
>
>
>
>
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