2012/12/11 terry mcintyre <[email protected]> > > > > > From: Darren Cook <[email protected]> > > > >> How much [effort] to determine whether there are multiple peaks? > <snippage> > > >> Now the tough question: How can this information be used to improve > move > > selection? > > > > One approach, not at all sophisticated, is better time management: spend > > less time on normal distributions, more time when the distribution is > > messy. (But I wonder if more time will just make the two peaks stand > > out more?) > > > If "more time" means "more of the same simulations", it might simply lead > to filling in the details; the peaks exist (hypothetically) because the > playouts are doing something non-optimal. > > I wonder if post-game review can focus on such positions and learn > something which would optimize the playouts. If such meta-analysis is quick > enough, it could be done during the game itself. >
Live-death is a problem of finding a correct sequence of playouts. The search tree may solve the live-death problem at the top side of tree. However, the sequence of moves that solves the live-death problem may leads to a losing result. Then, the search tree will tend to select other moves, and leave the live-death problem unsolved at the bottom side of tree, which "thinks" I have opportunities that you may make mistake at the endgame. This results in many peaks in the distribution. To improve the algorithms on live-death problems, a self-learning approach that can learn the sequence of playouts from the search tree is probably required. Even if the live-death problem is left to the bottom side of search tree, the playouts should still solve the live-death problem by a learned sequence of playouts. And the learned sequence of playouts may come from the search tree itself somewhere. It is still an open problem, and might be a potential future works. -- Chin-Chang Yang
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