I would add 4. The program tries to identify good moves, and only tries moves that it thinks might be good. If it is goal-directed, the good moves are good for a reason, and if the reason is not satisfied, they are discarded.
This is the way Many Faces works. It's very similar to 3, but it's a different way of thinking about the problem (adding good moves rather than deleting bad ones). You are correct that this approach is inadmissible, and is self limiting. I like it because when I add new knowledge I know I'm making the program stronger. I don't like tuning parameters or algorithms and then playing hundreds of games to get statistically significant data on the better value. I'm not saying my approach better, just that I prefer it :) David > > 3. selective in the "true" sense. Such a program tries to > identify bad moves and prune them from the tree, but they > are pruned permanently. NO matter how deep or long you > search they will never be considered. > > I think "true" selective programs, unless the pruning > criteria is fully admissible, is self limiting. You can > probably build a strong program but you will be bound > strictly to the quality > of your selective algorithm. Such an algorithm would play > imperfectly even on an infinte speed computer. > > UCT is admissible - it will ALWAYS find a winning move if you > are in a winning position. > > - Don > > > _______________________________________________ > computer-go mailing list > [email protected] > http://www.computer-go.org/mailman/listinfo/computer-go/ > _______________________________________________ computer-go mailing list [email protected] http://www.computer-go.org/mailman/listinfo/computer-go/
