I don't think traditional go programs "tally features and weights". They estimate the final score.
There have been prior global game tree approaches. Handtalk and GO Intellect and SmartGo did global searches a decade ago. This is not to detract from UCT, which works very well. UCT/MC programs make moves that look very unnatural, so in that sense they don't play at all like humans play go. David > -----Original Message----- > From: [EMAIL PROTECTED] [mailto:computer-go- > [EMAIL PROTECTED] On Behalf Of Don Dailey > Sent: Tuesday, December 11, 2007 11:53 AM > To: computer-go > Subject: Re: [computer-go] How does MC do with ladders? > > Hi Petri, > > I happen to think that MC is the most human like approach currently > being tried. > > The reason I say that is that humans DO estimate their winning chances > and "tally" methods, where you simply tally up features/weights > (regardless of how sophisticated) is not how strong humans think about > the game. > > Also, the best first global game tree approach, whatever you call it > such as UCT and others, is a very close model of how humans play the > game too. We may notice 3 moves that look playable, but gradually > come to focus on just 2 of those. Essentially monte carlo does this > too. Very narrow focused trees. > > The play-out portion is a crude approximation for imagination. We > basically look at a board and imagine the final position. The MC > play-outs kill the dead groups in a reasonably accurate (but fuzzy) way > and put the flesh on the skeleton. Near the end of the game, the > play-outs end mostly the same the way the game itself would end - and > the same way a human would expect it to look like. > > I attribute the success of MC to the fact that it's the best simulation > of how WE do it. The other approaches are clearly more synthetic, > including raw MC without a proper tree. > > - Don > > > Petri Pitkanen wrote: > > 2007/12/11, terry mcintyre <[EMAIL PROTECTED]>: > > > >> With Go, there are many situations which can be read out precisely, > provided > >> that one has the proper tools - ladders, the ability to distinguish > between > >> one and two eyes; the ability to reduce eyespaces to a single eye > with an > >> appropriate placement; and so forth. Failure to recognize such > situations is > >> like failing to spot a pinned piece or a passed pawn. > >> > >> > > > > I am no fan on MC approach but basically MC can read L&D given enough > > of simulations. It will read them without knowing that they need to > be > > analysed. Point in MC being that once you get more power you get > > better L&D as well, but without extra coding. > > > > This approach will result in non-human like game BUT likewise chess > > programs did not get strong by emulating humans. They just took one > > simple thing humans do and took it to extreme. Whatever approach will > > do the trick in go it will be similar in this sense. > > > > > _______________________________________________ > 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/
