I am amazed at the rating that Valk3.5_100 got with just 100 playouts/move. There are usually around 50 candidate move, and 100 playouts would give each candidate just 2 playouts. It still amazes me how you can get reasonable statistics with just 2 playouts. Statistically, this is unthinkable. I am guessing that your playout engine has a lot of Go ability in itself, so that every playout gives meaningful feedback, instead of relying on the statistics of lots of playouts. Please enlight me.
Regards, Fuming On Wed, Sep 8, 2010 at 5:37 AM, <[email protected]> wrote: > Quoting Dave Dyer <[email protected]>: > > >> But you have never (to my knowledge) layed out what way that is. >>> >> >> You're quite right here. I'm not advocating a specific change, just >> pointing out that all the effort going into building faster monte carlo >> engines may be irrelevant, because the programs actually need better >> steering. >> > > I have been working on Valkyria since 2006. Everytime I do something it > becomes slower. Meanwhile it has become about 1000 Elo points stronger (Only > 200 Elo is due to faster computer). If you talk about people on running > their programs on as large clusters as possible, then I may agree, but > otherwise I think you misunderstand completely what people are doing to > improve their programs. > > > I know we disagree on this point, but I believe chess has reached it's >> current state of success MOSTLY because of Moore's law. >> >> It always was believed that Go was would have to be solved by other >> means, perhaps even (gasp!) understanding the game. Monte carlo has >> given some credibility to the theory that Moores law may be enough after >> all. I'm arguing not. >> > > Monte-carlo search *is* the "other means". Random exploration is exactly > what I do when I play go. The only difference is that my search is goal > directed so many playouts is just a 3-10 ply deep locally. As consequence I > am weaker than MC-program in actually evaluating the whole board position. > This weakness means I have to painfully compensate for it by counting > territory to set the ambition for the goals I search. I sometime have a > great intutions about playing some vital point. This caused by nothing else > but the human variant of AMAF. > > Sure I do have a rich set of concepts that pop up in my thinking. But I am > afraid that this are just labels that I attach to my search results. I think > higher level concepts are very important for communicating about go, but > they are irrelevant for actually playing well. > > The kind of knowledge about go that actally is essential for computers and > humans is the ability to play tactically correct quick and without error. > This means undrstanding L&D, seki, semeai, ladders and so on. And this is > also what makes Valkyria strong. > > The reason Valkyria is not yet unbeatable is that the knowledge the > playouts have is still on a kyu level and very fragmented. There are > situations where I see the obious move in an instant where Valkyria needs to > search using several 100 playouts to get it right. In many cases it plays > perfect 100% of the time. > > Get the fundmental knowledge right + MCTS = strong go > > This has nothing to do with Moores Law. Valk3.5_100 is rated 1881 for 9x9 > which is stronger than Gnugo. It only plays 100 playouts. When I started > doing MC evaluation with Viking5 in 2005 I had to spend 100000 playouts to > get close to beating gnugo. > > Just another perspective > Magnus > > > > > _______________________________________________ >> Computer-go mailing list >> [email protected] >> http://dvandva.org/cgi-bin/mailman/listinfo/computer-go >> >> > > _______________________________________________ > Computer-go mailing list > [email protected] > http://dvandva.org/cgi-bin/mailman/listinfo/computer-go >
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