Weston Markham wrote:
I think that I have seen this sort of thing with Monte Carlo programs, and I think it is possible to get even less than "almost nothing". You may be getting overly-precise measurements of the Monte Carlo values of the moves near the beginning of the game, so that the played moves are biased toward lines of play where the Monte Carlo values are unrealistically good. (This could be thought of as being somewhat analogous to a "horizon effect".)
"less than almost nothing" and "overly precise"? That doesn't make any sense to me. At any point in the simulation run, the MC value will be a noisy representation of the value at it's limit (infinite simulations).
Put another way, the Monte Carlo values do tend to accurately distinguish the relatively good moves from the relatively poor moves, (which of course makes them very useful) but at any given position, you can't expect them to give the best score to the best move, even in the limit. As you run additional playouts, you can be more and more confident that your program has identified the move with the best Monte Carlo value. But suppose that there are other moves that are equally good (or better) under perfect play (or against a particular opponent). Then any supposed superiority of the program's selected move over those alternatives is entirely due to inaccuracies of the Monte Carlo value. So once you are running enough playouts to detect those differences, it also becomes more likely that subsequent positions will encounter these same inaccuracies.
No one ever alleged that pure AMAF or pure MC was infinitely scalable. _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
