Skip to main content

Intelligent agent for real-world applications on robotic edutainment and humanized co-learning


Dynamic assessment with an intelligent agent can differentiate the capabilities and proficiency of students. It can therefore be advocated as an interactive approach to conduct assessments on students in learning systems. Facebook AI Research proposed ELF OpenGo, an open-source reimplementation of the AlphaZero algorithm. They also developed Darkforest, which displays the competence and skills of high-level amateur Go players. To enable these open-source AI bots to assist humans at different levels in learning Go, this paper proposes an intelligent agent for real-world applications in robotic edutainment and humanized co-learning. To achieve this, we successfully constructed an OpenGo Darkforest (OGD) cloud platform using these AI bots and further combined the brain computer interface with the OGD cloud platform to observe the relationship between the brainwaves and win rates of human Go players. The intelligent agent also converted human brainwaves into physiological indices and reflected these in the robot to express human feelings or emotions in real-time. For future educational applications, this paper also presents intelligent robot teachers learning together with students in Taiwan and Japan. More than 200 students have been co-learning with intelligent robot teachers in Tainan, Kaohsiung, Taipei, and Tokyo from 2018 to 2019. The learning performance and feedback from students and teachers has been extremely positive, especially from remedial students.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18


  1., Zenbo (2019) Accessed 21 May 2019

  2. Brain Rhythm Inc. (2018) Accessed 21 May 2019

  3. Chuang CH, Huang CS, Ko LW, Lin CT (2015) An EEG-based perceptual function integration network for application to drowsy driving. Knowl Based Syst 80:143–152

    Article  Google Scholar 

  4. Embretson SE, Reise SP (2000) Item Response Theory. Taylor & Francis

  5. IEEE CIS (2016) 1855-2016-IEEE standard for fuzzy markup language. Accessed 21 May 2019

  6. Ko LW, Komarov O, Hairston WD, Jung TP, Lin CT (2017) Sustained attention in real classroom settings: an EEG study. Front Hum Neurosci 11:388

    Article  Google Scholar 

  7. Lee CS, Wang MH, Ko LW, Kubota N, Lin LA, Kitaoka S, Wang YT, Su SF (2018a) Human and smart machine co-learning: brain–computer interaction at the 2017 IEEE International Conference on Systems, Man, and Cybernetics. IEEE Syst Man Cybern Magz 4(2):6–13

    Article  Google Scholar 

  8. Lee CS, Wang MH, Wang CS, Teytaud O, Liu JL, Lin SW, Hung PH (2018b) PSO-based fuzzy markup language for student learning performance evaluation and educational application. IEEE Trans Fuzzy Syst 26(3):2618–2633

    Article  Google Scholar 

  9. Lee CS, Wang MH, Huang TX., Chen LC, Huang YC, Yang SC, Tseng CH, Hung PH, Kubota N (2018c) Ontology-based fuzzy markup language agent for student and robot co-learning. In: 2018 World Congress on computational intelligence (IEEE WCCI 2018), Rio de Janeiro, Brazil, pp 8–13

  10. Lee CS, Wang MH, Chen LC, Nojima Y, Huang TX, Woo J, Kubota N, Sato-Shimokawara E, Yamaguchi T (2019a) A GFML-based robot agent for human and machine cooperative learning on game of Go. In: 2019 IEEE Congress on Evolutionary Computation (IEEE CEC 2019), Wellington, New Zealand, pp 10–13

  11. Lee CS, Wang MH, Ko LW, Tsai BY, Yang SC, Lin LA, Lee YH, Ohashi O, Kubota N, Shuo N (2019b) PFML-based semantic BCI agent for game of Go learning and prediction.

  12. Lin CT, Chuang CH, Huang CS, Tsai SF, Lu SW, Chen YH, Ko LW (2014) Wireless and wearable EEG system for evaluating driver vigilance. IEEE Trans Biomed Circ Syst 8(2):165–175

    Article  Google Scholar 

  13. Mayfield Brain & Spine (2018) Anatomy of the brain. Accessed 21 May 2019

  14. Rogers C (2017) Me, myself and AI: are robot teachers in our future. Accessed 21 May 2019

  15. Sanders L (2018) Brain waves may focus attention and keep information flowing. Accessed 21 May 2019

  16. Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, Hubert T, Baker L, Lai M, Bolton A, Chen Y, Lillicrap T, Fan H, Sifre L, Driessche G, van den Graepel T, Hassabis D (2017) Mastering the game of go without human knowledge. Master 550:354–359

    Google Scholar 

  17. Stemberg RJ, Grigorenko EL (2001) Practical intelligence and the principal. (2001)

  18. Takase N, Takeda T, Botzheim J, Kubota N (2015) Interaction, communication, and experience design in robot edutainment. In: 6th International Conference on Advanced Mechatronics (ICAM2015), Waseda University, Tokyo, Japan, December 5–8, pp 159–160

  19. Tian Y, Zhu Y (2016) Better computer Go player with neural network and long-term prediction.

  20. Tian Y, Ma J, Gong Q, Sengupta S, Chen Z, Pinkerton J, Zitnick CL (2019) ELF OpengGo: An analysis and open reimplementation of AlphaZero.

  21. Tiwari N, Edla DR, Dodia S, Bablani A (2018) Brain computer interface: a comprehensive survey. Biol Inspir Cognit Architect 26:118–129

    Google Scholar 

  22. Wang MH, Wang CS, Lee CS, Lin SW, Hung PH (2014) Type-2 fuzzy set construction and application for adaptive student assessment system. 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2014), Beijing, July 6-11

  23. Yorita A, Kubota N (2011) Mutual learning for second language education and language acquisition of robots. In: Proceedings of the 6th international symposium on autonomous minirobots for research and edutainment, S32, Bielefeld, Germany, May 23-25

  24. Yorita A, Hashimoto T, Kobayashi H, Kubota N (2009) Remote education based on robot edutainment. In: Proceedings of the 5th international symposium on autonomous minirobots for research and edutainment, Incheon, Korea, August 16–18, pp 204–213

Download references


The authors would like to thank financial support from Ministry of Science Technology under three Grants MOST 106-3114-E-024-001, 107-2218-E-024-001, and 108-2218-E-024-001. Additionally, the authors would like to thank Tainan City Government of Taiwan, the involved staff of KWS research center, OASE Lab. members, Go players, including Yi-Hsiu Lee (8P), Hirofumi Ohashi (6P), Yu-Lin Lin (7D), and Yu-Hao Huang (2D), students, and teachers, including Tien-Tang Chang, Chien-Hsun Tseng, Pei-Yu Lee, Chia-Hui Wu, Yi-Ting Yang, and Chi-Jung Lee. Finally, we would like to thank Dr. Yuandong Tian and Facebook AI Research (FAIR) ELF OpenGo/Darkforest team members for their open source and technical support.

Author information



Corresponding author

Correspondence to Chang-Shing Lee.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lee, CS., Wang, MH., Tsai, YL. et al. Intelligent agent for real-world applications on robotic edutainment and humanized co-learning. J Ambient Intell Human Comput 11, 3121–3139 (2020).

Download citation


  • Intelligent agent
  • Dynamic assessment
  • Humanized co-learning
  • Robot edutainment
  • Brain–computer-interface