IIRC, Hendrik Baier's master thesis shows an experiment conducted using Orego that establishes that Dynamic Komi improved Orego's results against GnuGo. These are non-handicap games.
I recall that others have reported the same thing (that dynamic komi helps even in non-handicap games). IMO, Nick's entropy argument provides good theoretical reasons why it might work. The policy also has practical support, from the idea that adjusting komi keeps the program playing aggressively. Brian -----Original Message----- > In a Monte-Carlo program, the amount of information derived from one playout is given by its entropy > -p(win).log_2(p(win)) - p(lose).log_2(p(lose)). > This has a maximum at p(win) = 0.5, and is 0 if p(win) is 0 or 1. > > Similarly, suppose its best move has won less than 10% of the playouts. It could resign, but let's say it is giving a handicap to a weaker player. Instead of just doing more playouts, it can pretend that it will be receiving extra komi. Again, the quality of the information per playout then drops, but the quantity goes up, hopefully by more than enough to compensate. > > This seems like an argument for using dynamic komi, adjusted from time to time during each game move. > > Nick _______________________________________________ Computer-go mailing list [email protected] http://dvandva.org/cgi-bin/mailman/listinfo/computer-go
