IEEE Systems, Man and Cybernetics Magazine - January 2020 - 24

awarded additional points (komi) at the end of the game. In
general, humans may adopt different types of strategies for
an identical situation depending on their mood. They may
respond in a stable or an aggressive manner under a complicated game situation. A two-handicap difference exists
for human players between their good and bad statuses,
according to Yi-Hsiu Lee, a Go player with an 8p ranking
(professional player skill ranking, with 9p being the highest, for the traditional board game Go). The handicap
denotes what the weaker player is given to offset the
strength difference between players of different ranks.
Since AlphaGo was released in 2016 [1], humans have
been imitating and even memorizing surprising sequences
of the opening, thus gradually increasing the matching
degree of the opening between humans and smart machines.
Humans cannot win against a smart machine when the
actual game reaches the unknown middle game and the
end game; this is based on the premise that a smart
machine, a human's opponent, does not make errors or
execute incorrect code during the game. When a human's
situation is poor in a game, the machine usually steadily
controls the game to the end. Conversely, when a human's
situation is good, it is usually impossible for the human to
take the lead to the end.
As a result, artificial intelligence (AI) has engendered a
transformation in the game performance of professional Go
players. For example, they use top-level computer Go programs to analyze, review, learn, and predict the game situation after the game as if they had a personal professional Go
tutor at home. Many Go players have been adjusting to such
a transformation, and they also hope to share their ideas
and experiences with more people.
We established the OGD cloud platform [2], [3] to enable
humans to advance their Go-playing skills from amateur to
professional levels and enable smart machines to co-learn
Go. The human-smart machine co-learning OGD cloud
platform developed in cooperation with Japan has been utilized for the past three years, in the 2016 IEEE World Congress on Computational Intelligence (Vancouver, Canada),
2016 International Conference on Intelligent Robotics and
Applications (Tokyo, Japan), 2017 IEEE International Conference on Fuzzy Systems (Naples, Italy), IEEE SMC 2017
(Banff, Canada), and IEEE SMC 2018 (Miyazaki, Japan).
Human-Smart Machine Co-Learning Model
We designed different topics of learning demonstration,
including Go, BCI-based Go, mathematics, and language,
in our special event at IEEE SMC 2018 [4], [5]. The human
players learned from the suggestions provided by the
computer Go. The computer Go learned from the human
players through the designed co-learning models, Pair
Go, Team Go, and Reference Go. Figure 1 shows the
co- learning models that are described as follows. In
Pair Go, a human and a robot with an extensive, lightweight, and flexible (ELF) platform for game research
OpenGo engine [3] represent a black-white team. Each
24

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

player (human or robot) of the black-white team plays in
turns. The robot predicts the five possible outcomes of the
next move for the human's consideration. When it is the
human's turn to play, the robot would not report the suggested moves (i.e., the best outcome of the five possible
outcomes of the next move) so that the human plays
based on his or her thinking. Conversely, when it is the
robot's turn, the human helps his or her team member
play a stone by following the robot's top suggested moves.
The Pair Go game shows that the human learns from ELF
OpenGo, and the computer Go learns from human players
to replan its strategy.
In Team Go, the teams of white or black consist of
more than one human and a robot with an ELF OpenGo
engine. The human players are able to consult together
and refer to the top suggested move from the robot to
decide their final move to play. In Reference Go, the
white or black team is represented by a smart machine.
During the game, the robot first reports the top suggested
move as a reference to the human Go player. The human
player then considers his or her move by following or
rejecting the robot's suggested move.
In addition to the event held in Japan, we conducted a
Go co-learning activity in Taiwan on 4 October 2018
(Figure 2) [4]. The total number of games in this activity
was 12, and the activity included six Pair Go, three Team
Go, and three Reference Go games.
Wearable BCI System With Go
Because the Go game is a highly competitive and time-consuming activity, each right or wrong step highly affects the
probability of winning or losing the game. The game also
demands high levels of neural activity from a player, as
measured based on physiological state. Therefore, we utilized a novel wearable BCI system-developed by the Neural Engineering Laboratory of National Chiao Tung
University-that can actively monitor a Go player's physiological states and present five indicators: attention, stress,
fatigue, left-brain activation, and right-brain activation.
As shown in Figure 3(a), Shen-Su Chang wore the wearable BCI system while playing Go. Through real-time processing in our wearable BCI system, we could clearly
observe that when he was thinking about how to play a
stone on the board, his attention level was high, the
increasing activation of the right side of his brain was
associated with emotional processing, and his stress and
fatigue levels were low at this stage, as shown in FigureĀ 3(b). The benefit of the wearable BCI system is that it
can provide the five main physiological indicators to the
player and be integrated with neurofeedback or training
methods to change the Go player's physiological states to
avoid unnecessary faults and overcome the highly competitive pressure in the game. Moreover, we explored the neural activity changes associated with the human-machine
co-learning mechanism during the learning, training, and
practicing phases of education.



IEEE Systems, Man and Cybernetics Magazine - January 2020

Table of Contents for the Digital Edition of IEEE Systems, Man and Cybernetics Magazine - January 2020

Contents
IEEE Systems, Man and Cybernetics Magazine - January 2020 - Cover1
IEEE Systems, Man and Cybernetics Magazine - January 2020 - Cover2
IEEE Systems, Man and Cybernetics Magazine - January 2020 - Contents
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 2
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 3
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 4
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 5
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 6
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 7
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 8
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 9
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 10
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 11
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 12
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 13
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 14
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 15
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 16
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 17
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 18
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 19
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 20
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 21
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 22
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 23
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 24
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 25
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 26
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 27
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 28
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 29
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 30
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 31
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 32
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 33
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 34
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 35
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 36
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 37
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 38
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 39
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 40
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 41
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 42
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 43
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 44
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 45
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 46
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 47
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 48
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 49
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 50
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 51
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 52
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 53
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 54
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 55
IEEE Systems, Man and Cybernetics Magazine - January 2020 - 56
IEEE Systems, Man and Cybernetics Magazine - January 2020 - Cover3
IEEE Systems, Man and Cybernetics Magazine - January 2020 - Cover4
https://www.nxtbook.com/nxtbooks/ieee/smc_202110
https://www.nxtbook.com/nxtbooks/ieee/smc_202107
https://www.nxtbook.com/nxtbooks/ieee/smc_202104
https://www.nxtbook.com/nxtbooks/ieee/smc_202101
https://www.nxtbook.com/nxtbooks/ieee/smc_202010
https://www.nxtbook.com/nxtbooks/ieee/smc_202007
https://www.nxtbook.com/nxtbooks/ieee/smc_202004
https://www.nxtbook.com/nxtbooks/ieee/smc_202001
https://www.nxtbook.com/nxtbooks/ieee/smc_201910
https://www.nxtbook.com/nxtbooks/ieee/smc_201907
https://www.nxtbook.com/nxtbooks/ieee/smc_201904
https://www.nxtbook.com/nxtbooks/ieee/smc_201901
https://www.nxtbook.com/nxtbooks/ieee/smc_201810
https://www.nxtbook.com/nxtbooks/ieee/smc_201807
https://www.nxtbook.com/nxtbooks/ieee/smc_201804
https://www.nxtbook.com/nxtbooks/ieee/smc_201801
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_1017
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0717
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0417
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0117
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_1016
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0716
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0416
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0116
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_1015
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0715
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0415
https://www.nxtbook.com/nxtbooks/ieee/systems_man_cybernetics_0115
https://www.nxtbookmedia.com