NUTN, Taiwan

TMU, Japan
Co-sponsors Co. Ltd.

Scope and Topics

(1)To integrate the open source FAIR Darkforest (DF) program of Facebook (USA) with Item Response Theory (IRT) of NUTN (Taiwan) to the new open Go system, namely Dynamic DF (DyNaDF, Dynamic Darkforest) Go system and ELFOpenGo Go System; (2) To integrate DyNaDF Go and ELFOpenGo with FujiSoft Robot of TMU (leading by Prof. Kubota Lab., Japan) namely Robotic DyNaDF Go system; (3) To invite professional Go players and amateur Go players to attend the activity to have Go game on site with machine. (4) To have special sessions in IEEE SMC 2018 to call for submissions from related researchers in Italy, UK, Canada, …, and so on; (5) To have special issues in some journals to attract more attentions of researchers.

Activity Format

(1) To have a special event, “Human Interactive Learning on Cybernetics” in IEEE SMC 2018 (2) To invite speakers; (3) To have three activities for “Robotic Open Go System / BCI-based Serious Games System for Human Interactive Learning on Cybernetics” on site of IEEE SMC 2018.
Robotic BCI-DDF Go / BCI-based Serious Game + Human / Taiwan vs. Robotic BCI-DDF Go BCI-based Serious Game + Human / Taiwan
Robotic BCI-DDF Go BCI-based Serious Game + Human / Japan vs. Robotic BCI-DDF Go BCI-based Serious Game + Human / Taiwan
Robotic BCI-DDF Go BCI-based Serious Game + Human / Taiwan vs. Robotic BCI-DDF Go BCI-based Serious Game + Human / Japan 

Importance of Activity

Learning has become a very popular approach for cybernetics systems. This topic has always been considered a research in the Computational Intelligence area. Nevertheless, when talking about smart machines, it is not just about the methodologies. We need to consider systems and cybernetics. Sometimes, we also need to include human in the loop. Thus, it is definitely a research topic in SMC society.

About the series game, an intelligent robot agent based on domain ontology, machine learning mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning is presented. The machine-human co-learning model is established to help various students learn the mathematical concepts based on their learning ability and performance. Meanwhile, the robot acts as a teacher’s assistant to co-learn with children in the class. The FML-based knowledge base and rule base are embedded in the robot so that the teachers can get feedback from the robot on whether students make progress or not. Next, we infer students’ learning performance based on learning content’s difficulty and students’ ability, concentration level, as well as teamwork spirit in the class. Moreover, we combine the optimization techniques such as genetic algorithm (GA) and particle swarm optimization (PSO) with FML, called GFML and PFML, respectively, to learn the constructed knowledge base and rule base. Experimental results show that learning with the robot is helpful for disadvantaged and below-basic children [6].

The brainwave technology has been developed for a long time; however, applying it to play Go is the world’s first case in an IEEE conference. The world latest mobile and wireless EEG system is fully utilized in the innovation of the developed BCI-DDF Go system. The wireless system, developed by the research team from Brain Research Center in NCTU, is designed to extract the Go player’s brainwaves when they play and compete with the DDF Go system directly [3]. In the special event of IEEE SMC 2018 (, we will combine the theory of deep learning with the technology of BCI [1, 2] to demonstrate playing Go via cellphone.

In addition to Go system, we will hold Robotic BCI-based Game System for Human Interactive Learning on Cybernetics in IEEE SMC 2018. Meanwhile, we also have an associated special session on Semantic Web Technologies and Ontology for Real-World Applications to attract / encourage more researchers and scholars to submit their valuable papers to IEEE SMC 2018, to attend IEEE SMC 2018, to join SMC conference, and then to join the SMC society in the future.

We got the 3-year research project (Intelligent IRT Robot and Humans Co-Learning on Education and Learning Applications) from Ministry of Science and Technology (MOST, Taiwan). With the MOST research project fund, we will continue further to enhance the co-learning between humans and robots and will partially support the held activities in IEEE SMC 2018.

Smart machine is one of the important themes of IEEE SMC Society. It is good to use this competition of Professional Players vs. Machine and also have some cooperation between them to attract more attentions of worldwide scholars to SMC conferences. It definitely will have more papers submissions in this area to IEEE SMC 2018 and more researchers to join SMC society.

History of Activity

The year is the second year to hold Human & Smart Machines Co-Learning @ IEEE SMC. But for the human vs. computer Go competitions, the organizers have been held since 2008. For more details, you can refer to this video about the activities of Human vs. Computer Go from 2008 to 2016 funded by IEEE CIS, Taiwan government, National University of Tainan (NUTN) and Taiwanese Association for Artificial Intelligence (TAAI), Taiwan. The handicaps for the human vs. computer 19×19 game have been decreased from 29 in 1998 to 0 in 2016.
Standard Definition: 2008-2016 Human vs. Computer Go Video
High Definition: 2008-2016 Human vs. Computer Go Video
Past activities of Human vs. Computer Go from 2008 to 2016: Human vs. Computer Go
Human vs. Smart Machine @ IEEE SMC 2017:
Lu-An Lin shared her experience

Expected Humans

Professional Go Players
- Chun-Hsun Chou (9P / Taiwan)
- Yi-Hsiu Lee (8P / Japan)
- Hirofumi Ohashi (6P / Japan)
- Kai-Hsin Chang (5P / Taiwan)
- Maki Kaneko (1P / Japan)
- Yu-Lin Lin (7D / Taiwan)
- Shen-Su Chang (6D / Taiwan)
- Yu-Hao Huang (2D / Taiwan)
- Hideki Kato (1D / Japan)
- Hsien-Ming Chen (Kyu / Taiwan)

Expected Computer Go Programs
- Darkforest Open Source (DDF)
- ELFOpenGo Open Source (OGD)
Expected Panelists
- Hideki Kato (Japan)
- Kaname Tsukioka (Japan)
Download File
- Brief introduction to Human & Smart Machine Co-Learning



CCS, Japan


MOST, Taiwan

AIIAA, Taiwan

AIIAA, Taiwan

NCTU, Taiwan

NEL/NCTU, Taiwan



Haifong Weiqi Academy

Bureau of Education

Bureau of Education

Copyright © 2017 NUTN, Taiwan All Rights Reserved Last Updated: Nov. 12, 2018