Quoting Don Dailey <[EMAIL PROTECTED]>:
When the child nodes are allocated, they are done all at once with this code - where cc is the number of fully legal child nodes:
In valkyria3 I have "supernodes" that contains an array of "moveinfo" for all possible moves. In the moveinfo I also store win/visits and end position ownership statistics so my data structures are memory intensive. As a consequence I expand each move individually, and my threshold seems to be best at 7-10 visits in test against Gnugo. 40 visits could be possible but at 100 there is a major loss in playing strength.
Valkyria3 is also superselective using my implementation of mixing AMAF with UCT as the mogo team recently described. The UCT constant is 0.01 (outside of the square root).
When it comes to parameters please remember that they may not have independent effects on the playing strength. If one parameter is changed a lot then the best value for other parameters may also change. And what makes things worse is probably that best parameters change as a function of the playouts. I believe that ideally the better the MC-eval is the more selective one can expand the tree for example.
-Magnus _______________________________________________ computer-go mailing list [email protected] http://www.computer-go.org/mailman/listinfo/computer-go/
