Btw, I thought neural networks trained from games were ok with basic bad shapes because they "understand" the situation much better, but in some cases they still make incredible local blunders, so I guess they're affected too. Here's an example with Detlef's 54% dcnn:
A B C D E F G H J K L M N O P Q R S T +---------------------------------------+ 19 | . . . . . . . . . . . . . . . . . . . | 18 | . O . . . . . . . . . . . . . . . . . | 17 | . X O O . O . . . . . . . X . X . . . | 16 | . X X O . . . . . . . . . . . . X . . | 15 | . . . X O . . . . . . . . . . O X . . | 14 | . . X . . . . . . . . . . . . O O . . | 13 | . . O O . . . . . . . . . . . . . . . | 12 | . . O X X . . . . . . . . . . . . . . | 11 | . . O O X . . . . . . . . . . . . . . | 10 | . . X X X O . . . . . . . . . . O . . | 9 | . . . . X . . . . . . . . . . . . . . | 8 | . . O . . . . . . . . . . . . . X . . | 7 | . . . . . . . . . . . . . . . . . . . | 6 | . . . . . . . . . . . . . . . . . . . | 5 | . . O . . . . . . O . . . . . . . . . | 4 | . . . . . . . X)O X X . . . . . X . . | 3 | . . . O O . . X X O O X . . X . . . . | 2 | . . . X . . . O O O O X . . . . . . . | 1 | . . . . . . . . . O X X . . . . . . . | +---------------------------------------+Network is almost 100% sure that saving 1 stone in atari with J5 is the right move here, and loses all bottom 7 stones !
[ J5 M4 G3 G2 H5 ... [ 0.92 0.05 0.03 0.01 0.00 ... Matt On 04/17/2017 01:04 PM, Jim O'Flaherty wrote:
It seems chasing down good moves for bad shapes would be an explosion of "exception cases", like combinatorially huge. So, while you would be saving some branching in the search space, you would be ballooning up the number of patterns for which to scan by orders of magnitude. Wouldn't it be preferable to just have the AI continue to make the better move emergently and generally from probabilities around win placements as opposed to what appears to be a focus on one type of local optima?
game.sgf
Description: application/go-sgf
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