[Computer-go] Congratulations to CrazyStone!

ds ds2 at physik.de
Fri Mar 8 09:42:59 PST 2013

Can you provide a link to your thesis, as the one I found is dead:)

Thanks Detlef

Am Freitag, den 08.03.2013, 00:30 +0000 schrieb Aja Huang:
>           Now it seems to me that this is related to the way playouts
>         are done
>         and it will be difficult to improve with Mogo style
>         (rule-based)
>         playouts above certain strength, without using larger patterns
>         and next
>         move choice based on probability distribution. Currently,
>         playing out
>         a simple joseki in a sensible way in simulations will just
>         never happen.
>         This is a bit frustrating since all my attempts at
>         successfully
>         implementing probdist-based playouts have failed so far, but I
>         guess
>         I will just have to try again... 
> To implement softmax, you can refer to my thesis where I have
> described the framework of the move generator for the playout.
> Detecting forbidden moves and replacing useless moves by better
> alternatives are very useful. There you can gain a lot by applying
> much Go-knowledge. Two good candidate algorithms for training the
> feature weights are MM and SB(Simulation Balancing). I tried hard but
> failed to measure any improvement from SB gammas (trained on 9x9) on
> 19x19. You can use CLOP to tune the MM gammas which are far from
> optimal according to our experience.
> Also, my regression test of seki and L&D that pachi has participated
> could be helpful to improve program's tactical strength. In my
> opinion, that is the most crucial factor to reach high-dan level.
> Cheers,
> Aja
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