Hi; sorry for taking some of your time with non-technical long-term AI/GO dreaming, but if sometimes you find Go fascinating you might like the video below :-)
As many of you I guess, I've spent time trying to design some sort of learning in MCTS, so that monte-carlo simulations would be "adaptive" to the current situation. This idea looks like a very natural solution to the problems we have for reaching human top-level. I've met this incredible game; I'm not a Go player, but like many not-so-strong players at first view the moves by black look like a big mistake (misunderstood ladder): In fact, it's (as far as I see...) a very clever idea by black (Lee Sedol, pro player), in spite of the fact that it's a failed ladder. http://www.youtube.com/watch?v=beic62XoHnM We tried various things for having machine learning in MCTS: - Contextual Monte-Carlo for online learning simulations: http://hal.inria.fr/inria-00456422/ - poolRave (using RAVE values in simulations): http://hal.inria.fr/inria-00485555/ - Bernstein Races for offline learning patterns http://hal.inria.fr/inria-00622150/ (a synthesis of these papers in http://hal.inria.fr/inria-00544758/ ) and many of you have published related stuff; but when a computer will be able to understand a situation as the game above, it will be very impressive to me :-) Go looks like a combination between feeling and mathematical reasoning. One day the people of the Go-sect will convince me that this game has something really special :-) In particular, my feeling is that a 10kyu can not play this pro game, but a 10-kyu can understand a posteriori. It's difficult the discuss the possibility for a computer to understand a posteriori, but with a little bit of provocation from this point of view computers are not yet 10-key :-) Best regards, Olivier
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