Go ahead and do it then and produce this super-bot. I know that the programmers of the top engines have tried many things that have weakened the engine. This could just as easily be one of those as it could be one that improves it. Maybe even easier. Only way to tell is to try it out (with bug-free code).
On Dec 6, 2007 4:20 PM, terry mcintyre <[EMAIL PROTECTED]> wrote: > > > From: Nick Wedd <[EMAIL PROTECTED]> > > > >>which will alter the score not by one point, but by ten or twenty. > >>Their estimate of winning probability is totally wrong. Good players > >>winnow out losing moves and stick with good moves - the basic premise > >>of minimax searching. Losing a big group will lead to a win only if one > >>obtains equivalent compensation elsewhere. Good players sometimes make > >>sacrifice plays, but failing to recognize that one's group is lost will > >>totally skew one's estimate of one's winning chances. > > >We all know that MC programs don't play perfectly. What point are you > >making? > > I begin with the empirical observation that existing MC programs can be very > wrong in their estimation of the likely score of given positions, and > therefore > of the winning probabilities of the various moves. Consider a group which > dies > due to a skillful placement. If I know about this irrefutable move, I > estimate that > your group is dead, and the score is 20 points in my favor. If I do not know > that > move, my estimate might be that the game is yours by half a point. > > Now, in a universe of equally blind bots, a poor estimate can be better than > a > slightly worse estimate; but to compete against pro human players, one must > be > able to get fairly close to the correct score for stable positions. > > My point is that there appears to be a systemic error in the existing > estimation process. > Relating back to the move generation used by "random" playouts; amongst > skilled > players, it is quite common to consider ways to reduce groups to the > single-eyed state. > ( The Cotsen Go Tournament sweatshirts read "Cyclops Assassin" for this > reason. ) > > Under fast time controls, humans may fail to notice some nakade > opportunities. It is > also possible to exploit nakade, but to have a bot continue playing as if it > were still in > the game, eventually winning on time. When human players discover that it is > important > to explicitly kill such groups, the winning rates of such bots will plummet. > > I suggest that a useful stage in the evolution of "heavy playouts" might be > to incorporate > provably-correct analysis of group status, placements, and "fighting spirit" > heuristics. This would > skew the distribution of playouts toward that actually used by skilled > players, improving the accuracy > of the estimation. As my friend and coach Chris H often says: "When in > doubt, read it out." > > > ________________________________ > Looking for last minute shopping deals? Find them fast with Yahoo! Search. > _______________________________________________ > computer-go mailing list > computer-go@computer-go.org > http://www.computer-go.org/mailman/listinfo/computer-go/ > _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
