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 > > _______________________________________________ > Computer-go mailing list > [email protected] > http://dvandva.org/cgi-bin/mailman/listinfo/computer-go _______________________________________________ Computer-go mailing list [email protected] http://dvandva.org/cgi-bin/mailman/listinfo/computer-go
