For that reason I guess that AlphaGo opening style is mostly influenced by the net that is trained on strong human games, while as the game progresses the MC rollouts have more and more influence in choosing a move. Is my understanding way off? On Mar 10, 2016 12:40 PM, "Thomas Wolf" <tw...@brocku.ca> wrote:
> But at the start of the game the statistical learning of infinitessimal > advantages of one opening move compared to another opening move is less > efficient than the learning done in the middle and end game. > > On Thu, 10 Mar 2016, Sorin Gherman wrote: > > >> From reading their article, AlphaGo makes no difference at all between >> start, middle and endgame. >> Just like any other position, the empty (or almost empty, or almost full) >> board is just another game position in which it chooses (one of) the most >> promising moves in order to maximize her chance of winning. >> >> On Mar 10, 2016 12:31 PM, "uurtamo ." <uurt...@gmail.com> wrote: >> >> Quick question - how, mechanically, is the opening being handled by >> alpha go and other recent very strong programs? Giant hand-entered or >> game-learned joseki books? >> >> Thanks, >> >> steve >> >> On Mar 10, 2016 12:23 PM, "Thomas Wolf" <tw...@brocku.ca> wrote: >> My 2 cent: >> >> Recent strong computer programs never loose by a few points. >> They are either >> crashed before the end game starts (because when being >> clearly behind they play more >> desperate and weaker moves because they mainly get negative >> feadback from >> their search with mostly loosing branches and risky play >> gives them the only >> winning sequences in their search) or they win by resignation >> or win >> by a few points. >> >> In other words, if a human player playing AlphaGo does not >> have a large >> advantage already in the middle game, then AlphaGo will win >> whether it looks >> like it or not (even to a 9p player like Michael Redmond was >> surprised >> last night about the sudden gain of a number of points by >> AlphaGo in the >> center in the end game: 4:42:10, 4:43:00, 4:43:28 in the >> video https://gogameguru.com/alphago-2/) >> >> In the middle and end game the reduced number of possible >> moves and the >> precise and fast counting ability of computer programs are >> superior. In the >> game commentary of the 1st game it was mentioned that Lee >> Sedol considers the >> opening not to be his strongest part of the game. But with >> AlphaGo playing >> top pro level even in the opening, a large advantage after >> the middle game >> might simply be impossible to reach for a human. >> >> About finding weakness: >> In the absense of games of AlphaGo to study it might be >> interesting to get a general idea by checking out the games where 7d Zen >> lost on KGS >> recently. >> >> Thomas >> >> On Thu, 10 Mar 2016, wing wrote: >> >> One question is whether Lee Sedol knows about these >> weaknesses. >> Another question is whether he will exploit those >> weaknesses. >> Lee has a very simple style of play that seems less >> ko-oriented >> than other players, and this may play into the hands of >> Alpha. >> >> Michael Wing >> >> I was surprised the Lee Sedol didn't take the >> game a bit further to >> probe AlphaGo and see how it responded to >> [...complex kos, complex ko >> fights, complex sekis, complex semeais, >> ..., multiple connection >> problems, complex life and death problems] as >> ammunition for his next >> game. I think he was so astonished at being put >> into a losing >> position, he wasn't mentally prepared to put >> himself in a student's >> role again, especially to an AI...which had >> clearly played much weaker >> games just 6 months ago. I'm hopeful Lee Sedol's >> team has been some >> meta-strategy sessions where, if he finds >> himself in a losing position >> in game two, he turns it into exploring a set of >> experiments to tease >> out some of the weaknesses to be better >> exploited in the remaining >> games. >> >> On Thu, Mar 10, 2016 at 8:16 AM, Robert Jasiek < >> jas...@snafu.de> wrote: >> >> > On 10.03.2016 00:45, Hideki Kato wrote: >> > > > such as solving complex semeai's and >> double-ko's, aren't solved yet. >> > > To find out Alphago's weaknesses, there can >> be, in particular, >> > > - this match >> > - careful analysis of its games >> > - Alphago playing on artificial problem >> positions incl. complex kos, > complex ko fights, complex >> sekis, complex semeais, complex endgames, > >> multiple connection problems, complex life and death >> problems (such as > Igo Hatsu Yoron 120) etc., >> and then theoretical analysis of such play >> > - semantic verification of the program code >> and interface >> > - theoretical study of the used theory and the >> generated dynamic data > (structures) >> > > -- >> > robert jasiek >> > _______________________________________________ >> > Computer-go mailing list >> > Computer-go@computer-go.org >> > >> http://computer-go.org/mailman/listinfo/computer-go [1] >> >> >> >> Links: >> ------ >> [1] >> http://computer-go.org/mailman/listinfo/computer-go >> >> _______________________________________________ >> Computer-go mailing list >> Computer-go@computer-go.org >> >> http://computer-go.org/mailman/listinfo/computer-go >> >> _______________________________________________ >> Computer-go mailing list >> Computer-go@computer-go.org >> http://computer-go.org/mailman/listinfo/computer-go >> >> >> _______________________________________________ >> Computer-go mailing list >> Computer-go@computer-go.org >> http://computer-go.org/mailman/listinfo/computer-go >> >> >> _______________________________________________ >> Computer-go mailing list >> Computer-go@computer-go.org >> http://computer-go.org/mailman/listinfo/computer-go >> >> >> > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go >
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