Le 26/11/2009 à 10:08, Vlad Dumitrescu a écrit : > > On Thu, Nov 26, 2009 at 00:43, Darren Cook <[email protected]> wrote: > > When I read this it reminded me of experiments I tried before to pass > > more than one piece of information up from the leaf nodes of a (min-max) > > tree. E.g. a territory estimate and an influence estimate. I gave up as > > it got too complex to handle incomparable nodes (e.g. move A gets more > > territory, less influence). I remember having a really good reason to > > want to delay reducing multiple features to a single number , but it is > > all a bit fuzzy now. > > > > Does this type of search have a name, and any associated research? > > It feels like something that should be covered by some mathematical > area, but I spent half of last night searching in vain. It's possible > that I just didn't understand what those papers said. > > One issue is that when the value at a node includes other features > than win/loss information, then the game is no longer zero-sum and > then maximin should be used instead of minimax. Also the linearization > of these feature's values should be done differently for each player > (even if we might choose to play a little recklessly ourselves, for > the opponent we should probably use the safest line of play).
Maybe have a look at signal processing, using higher-orders statistics ? mean std-deviation = order 2 (or 1 ?) ... win by 10 with std = 100 seems much less secure than win by 5 with std=1 but maybe this is included in modern bots (i stopped at naive MC + AMAF) It's far in my memory, so i can't tell you much more Alain _______________________________________________ computer-go mailing list [email protected] http://www.computer-go.org/mailman/listinfo/computer-go/
