IEEE Systems, Man and Cybernetics Magazine - January 2020 - 28

(a)

Winning Rate (%)

100
White
Black

75

B127: 37.7%
W128: 61.94%

50
25
0

1

11

21

31

41

51

61

71
81
Move No
(b)

91

101

111

121

131

141

Figure 5. The record of Game 12-2, with Hirofumi Ohashi (6p) and Maki Kaneko (1p) (Team Go) and the robot
(ELF OpenGo) as white, ELF OpenGo as black, and black winning the game: the (a) game record and (b) win rate curve.

that caused the white team to lose was move 128.
Actually, the robot showed that the recommended
position is N13, but we misunderstood and played
move 128 at N12. During the game, the robot suggested the move to the Go players and showed it on its
face using the coordinates (x, y). Nevertheless,
human Go players considered their next move with
the patterns, i.e., the images of the board that were
stored in their brains. Therefore, human Go players
had to spend some time translating the coordinates
(x, y) into the images of the board. I felt it is very
important and helpful for human Go players if the
robot can provide the suggested moves by using the
same interface as ours. Overall, AI is very powerful,
but it is not perfect yet. If humans can know the characteristics of AI, they will exhibit a better learning
performance. Nevertheless, humans also need to
improve their training to achieve this goal.
28

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Janu ar y 2020

Conclusion and Discussion
The OGD cloud platform was developed using a Facebook
open-source ELF OpenGo, BCI technology, and a robot
and was first applied to human-smart machine co-learning at IEEE SMC 2018. Figures 6-8 depict photos from this
special event.
Figure 9 presents the game records and win rate curves
for the seventh game of the 2018 Japanese Meijin Go Tournament, where the left-side game is the original one
(Chang Hsu was a member of the white team and Yuta
Iyama was on the black team, and the white team won the
game) [6]. Lee (8p) commented, "The white team endeavored to win up to move 172 and the black team made an
unexplainable mistake at move 171." Hence, to observe the
effect of human-smart machine co-learning, we incorporated two variations in this game. In the middle and righthand games presented in Figure 9, Lee (8p) followed the
robot's top suggested move to play for the black and white



IEEE Systems, Man and Cybernetics Magazine - January 2020

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