Re: computer-go: abstract info and neural nets

["Ran Xiao" <ranxiao@xxxxxxxxxxxx>]

Carl,

Thank you for all the articles about the NN. They all are very interesting
and helpful. I hope one day I can build an artificial brain likes Andrew of
the movie "Bicentennial Man".

I am using quasi 3-layer NN (a 3-layer NN without sigmoid function at the
output layer). The number of input nodes is 81 for 9x9 board. Right now, the
number of hidden nodes is 160. The number of output node is 1 which is the
plausible value [0, 1] for a given board for BLACK played.

For the board evaluation, my philosophy is that if the players anticipated
game correctly, the final score will be score for the first move. Thus, if
we have a correct full board evaluation function, no need to play GO.

The current training method thus is supervised quasi-reinforced. I am using
batch mode with back-propagation momentum-based weights modification. I was
thinking to use limited-memory BFGS.

Weimin

P.S. The following is a game I played with ForeverBlack for the latest
update of NN weight file. The NN training is still far from converge, so
ForeverBlack behaves different every time I updated its weight file. The
play is very casual. ForeverBlack does not count stones, I briefly counted
it myself.

Date Sun Jan 13 13:07:16 PST 2002
Boardsize 9
Handicap  0
Komi  5.5
Black ForeverBlack
White Human
Score 11  43
 B 0 D5
 W 1 F5
 B 2 E4
 W 3 E6
 B 4 A2
 W 5 F4
 B 6 F7
 W 7 E7
 B 8 B5
 W 9 C7
 B 10 A7
 W 11 E3
 B 12 C6
 W 13 D6
 B 14 D3
 W 15 E2
 B 16 B7
 W 17 C8
 B 18 H7
 W 19 G6
 B 20 F2
 W 21 G2
 B 22 G3
 W 23 F3
 B 24 C2
 W 25 D2
 B 26 D8
 W 27 E8
 B 28 G7
 W 29 H6
 B 30 E9
 W 31 F8
 B 32 D4
 W 33 B8
 B 34 H9
 W 35 G8
 B 36 B3
 W 37 A8
 B 38 H5
 W 39 H4
 B 40 G4
 W 41 J5
 B 42 E1
 W 43 D1
 B 44 B1
 W 45 C1
 B 46 G1
 W 47 F1
 B 48 C9
 W 49 H3
 B 50 D7
 W 51 D9
 B 52 D8
 W 53 F9
 B 54 D7
 W 55 D9
 B 56 D8
 W 57 B9
 B 58 J6
 W 59 G5
 B 60 B6