David Fotland wrote: > I don't think traditional go programs "tally features and weights". They > estimate the final score. > When I say "tally features and weights" I really mean that they estimate the final score. They basically tally the number of intersections expected to be won, although I'm sure there is a great deal of sophistication in this. I have seen descriptions where they estimate a groups survival likelihood, and if it's say 50% they give it 50% of the area encompassed.
> 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. > It's the approach I believe to be more human-like. Not necessarily the playing style. - Don > 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/ > > _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
