Skip to main content

Advertisement

Springer Nature Link
Log in

Adaptive Fuzzy Neural Agent for Human and Machine Co-learning

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

This paper proposes an Adaptive Fuzzy Neural Agent (AFNA) with a Patch Learning Mechanism and IEEE 1855 Fuzzy Markup Language (FML) for human and machine co-learning. There are three phases of patch learning mechanism embedded in AFNA, including (1) training an initial global model, (2) training a patch model for each identified patch, and (3) updating the global model using the training data that do not fall into any patch. The AFNA can be applied to construct the student and robot co-learning regression model, as well as the regression model for the dataset retrieved from the game of Go. First, students generate human learning data through interactions with handheld devices or robots based on the AFNA in Taiwan and Japan. Then, the AFNA utilizes the student learning data collected in the classroom and the Go game data provided by both Google DeepMind and Facebook AI Research open-source OpenGo to train the Fuzzy Machine-Learning Model. In addition, the trained Fuzzy Machine-Learning Model of AFNA is deployed to the robots to make students and machines co-learn together based on IEEE 1855 FML. The experiments show that the AFNA with Patch Learning Mechanism and Fuzzy Machine-Learning Model can improve the performance of regression model based on the datasets of student learning and Go game. In the future, we hope to apply the AFNA with robots to the other domain areas, embed it with the Artificial Intelligence of Things devices, and introduce it to more teaching fields in various countries.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Acampora, G., Loia, V.: Fuzzy control interoperability and scalability for adaptive domotic framework. IEEE Trans. Industr. Inf. 1(2), 97–111 (2005)

    Article  Google Scholar 

  2. Acampora, G., Loia, V., Lee, C.S., Wang, M.H.: On the Power of Fuzzy Markup Language. Springer-Verlag, Germany (2013)

    Book  Google Scholar 

  3. IEEE CIS.: 1855–2016-IEEE Standard for Fuzzy Markup Language (2016). https://ieeexplore.ieee.org/document/7479441.

  4. Acampora, G., di Stefano, B., Vitiello, A.: IEEE 1855TM: the first IEEE standard sponsored by IEEE Computational Intelligence Society. IEEE Comput. Intell. Mag. 11(4), 4–7 (2016)

    Article  Google Scholar 

  5. Lee, C.S., Wang, M.H., Tsai, Y.L., Ko, L.W., Tsai, B.Y., Hung, P.H., Lin, L.A., Kubota, N.: Intelligent agent for real-world applications on robotic edutainment and humanized co-learning. J. Ambient. Intell. Humaniz. Comput. 11, 3121–3139 (2019)

    Article  Google Scholar 

  6. Lee, C.S., Wang, M.H., Tsai, Y.L., Chang, W.S., Reformat, M., Acampora, G., Kubota, N.: FML-based reinforcement learning agent with fuzzy ontology for human-robot cooperative edutainment. Int. J. Uncertain. Fuzzin. Knowl.-Based Syst. 28(6), 1023–1060 (2020)

    Article  MathSciNet  Google Scholar 

  7. Lee, C.S., Wang, M.H., Yang, S.C., Hung, P.H., Lin, S.W., Shuo, N., Kubota, N., Chou, C.H., Chou, P.C., Kao, C.H.: FML-based dynamic assessment agent for human-machine cooperative system on game of Go. Int. J. Uncertain. Fuzzin. Knowl.-Based Syst. 25(5), 677–705 (2017)

    Article  MathSciNet  Google Scholar 

  8. Soto-Hidalgo, J.M., Alonso, J.M., Acampora, G., Alcalá-Fdez, J.: JFML: a java library to design fuzzy logic systems according to the IEEE std 1855–2016. IEEE Access 6, 54952–54964 (2018)

    Article  Google Scholar 

  9. Gámez-Granados, J.C., Rodriguez-Lozano, F.J., Acampora, G., Lee, C.S., Soto-Hidalgo, J.M.: JKinect: a new Java software for designing and assessing gross motor activities in children with autism based on JFML. In: 2020 World Congress on Computational Intelligence (IEEE WCCI 2020), Glasgow, Scotland, UK, Jul. 19–24 (2020)

  10. Lee, C.S., Tsai, Y.L., Wang, M.H., Kuan, W.K., Ciou, Z.H., Kubota, N.: AI-FML agent for robotic game of Go and AIoT real-world co-learning applications. In: 2020 World Congress on Computational Intelligence (IEEE WCCI 2020), Glasgow, Scotland, UK, Jul. 19–24, 2020.

  11. Arrieta, A.B., Diaz-rodrigues, N., Ser, J.D., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., Herrera, F.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inform. Fusion 58, 82–115 (2020)

    Article  Google Scholar 

  12. Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138–52160 (2018)

    Article  Google Scholar 

  13. Hagras, H.: Toward human-understandable, explainable AI. Computer 51(9), 28–36 (2018)

    Article  Google Scholar 

  14. Alonso, J.M., Castiello, C., Magdalena, L., Mencar, C.: Design and validation of an explainable fuzzy beer style classifier. In: Moral, J.M.A., Castiello, C., Magdalena, L., Mencar, C. (eds.) Explainable Fuzzy Systems: Paving the Way from Interpretable Fuzzy Systems to Explainable AI Systems Studies in Computational Intelligence, vol. 970, pp. 169–217. Springer, Cham (2021)

    Chapter  Google Scholar 

  15. Alonso, J.M.: Teaching explainable artificial intelligence to high school students. Int. J. Comput. Intell. Syst. 13(1), 974–987 (2020)

    Article  Google Scholar 

  16. Jang, J.S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)

    Article  Google Scholar 

  17. El-Hasnony, I.M., Barakat, S.I., Mostafa, R.R.: Optimized ANFIS model using hybrid metaheuristic algorithms for Parkinson’s disease prediction in IoT environment. IEEE Access 8, 119252–119270 (2020)

    Article  Google Scholar 

  18. Khan, M.A., Algarni, F.: A healthcare monitoring system for the diagnosis of heart disease in the IoMT cloud environment using MSSO-ANFIS. IEEE Access 8, 122259–122269 (2020)

    Article  Google Scholar 

  19. Ai-Hmouz, A., Shen, J., Ai-Hmouz, R., Yan, J.: Modeling and simulation of an adaptive neuro-fuzzy inference system (ANFIS) for mobile learning. IEEE Trans. Learn. Technol. 5(3), 226–237 (2012)

    Article  Google Scholar 

  20. Wu, D., Mendel, J.M.: Patch learning. IEEE Trans. Fuzzy Syst. 28(9), 1996–2008 (2020)

    Article  Google Scholar 

  21. Zakeri, A., Saberi, M., Hussain, O.K., Chang, E.: Addressing missing data and data competitiveness issues: transforming tacit knowledge into explicit form by fuzzy inference learning system. Int. J. Fuzzy Syst. 20, 1224–1239 (2018)

    Article  Google Scholar 

  22. van Zoelen, E.M., van den Bosch, K., Neerincx, M.: Becoming team members: identifying interaction patterns of mutual adaptation for human-robot co-learning. Front. Robot. AI 8, 692811 (2021)

    Article  Google Scholar 

  23. McMahan, B., Ramage, D.: Federated learning: collaborative machine learning without centralized training data. (2017). https://ai.googleblog.com/2017/04/federated-learning-collaborative.html.

  24. Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. 10(2), 1–19 (2019)

    Article  Google Scholar 

  25. Hwang, K.S., Lin, J.L., Shi, H., Chen, Y.Y.: Policy learning with human reinforcement. Int. J. Fuzzy Syst. 18, 618–629 (2016)

    Article  MathSciNet  Google Scholar 

  26. Lee, C.S., Wang, M.H., Ciou, Z.H., Chang, R.P., Tsai, C.H., Chen, S.C., Huang, T.X., Sato-Shimokawara, E., Yamaguchi, T.: Robotic assistant agent for student and machine co-learning on AI-FML practice with AIoT application. In: 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2021), Luxembourg, Luxembourg, Jul. 11–14 (2021)

  27. Lee, C.S., Kubota, N., Yamaguchi, T.: AI-FML Human and Machine Co-Learning. Acmebooks Publishing Co., Ltd, Taiwan (2020). (in Chinese)

    Google Scholar 

  28. Embretson, S.E., Steven, P.R.: Item Response Theory. Psychology Press, New York (2013)

    Book  Google Scholar 

  29. Lee, C.S., Wang, M.H., Wang, C.S., Teytaud, O., Liu, J.L., Lin, S.W., Hung, P.H.: PSO-based fuzzy markup language for student learning performance evaluation and educational application. IEEE Trans. Fuzzy Syst. 26(5), 2618–2633 (2018)

    Article  Google Scholar 

  30. Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., Hassabis, D.: Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016)

    Article  Google Scholar 

  31. DeepMind, AlphaGo Master series: 60 online games, Jan. (2020). https://deepmind.com/alphago-master-series.

  32. Tian, Y., Zhu, Y.: Better computer Go player with neural network and long-term prediction. In: 5th International Conference on Learning Representations (ICLR 2017), Toulon, France, Apr. 24–26 (2017).

  33. Tian, Y., Ma, J., Gong, Q., Sengupta, S., Chen, Z., Pinkerton, J., Zitnick, C.L.: ELF Open Go: an analysis and open reimplementation of AlphaZero. In: Proceedings of the 26th International Conference on Machine Learning Research (PMLR 2019), Vancouver, Canada, Dec. 8–14, vol. 97, pp. 6244–6253 (2019)

  34. 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., van den Driessche, G., Graepel, T., Hassabis, D.: Mastering the game of Go without human knowledge. Nature 550, 354–359 (2017)

    Article  Google Scholar 

  35. Lee, C.S., Tsai, Y.L., Wang, M.H., Kubota, N.: AI-FML agent with patch learning mechanism for robotic game of Go application. In: 2020 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2020), Toronto, Canada, Oct. 11–14, (2020)

Download references

Acknowledgements

The authors would like to thank the financial support sponsored by the Ministry of Science and Technology (MOST) of Taiwan under the grant MOST 109-2622-E-024-001-CC1 and MOST 108-2218-E-024-001. The authors would like to thank the staff of the KWS Center and OASE Lab. of NUTN as well as the involved faculty and students of Gueinan elementary school and Rende elementary school in Taiwan. Finally, the authors would like to thank Rinpin Chang for editing the videos adopted in this paper.

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National University of Tainan, Tainan, Taiwan

    Chang-Shing Lee, Yi-Lin Tsai, Mei-Hui Wang & Sheng-Hui Huang

  2. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Marek Reformat

  3. Department of Mechanical Systems Engineering, Tokyo Metropolitan University, Tokyo, Japan

    Naoyuki Kubota

Authors
  1. Chang-Shing Lee
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Yi-Lin Tsai
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Mei-Hui Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Sheng-Hui Huang
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Marek Reformat
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Naoyuki Kubota
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Chang-Shing Lee.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, CS., Tsai, YL., Wang, MH. et al. Adaptive Fuzzy Neural Agent for Human and Machine Co-learning. Int. J. Fuzzy Syst. 24, 778–798 (2022). https://doi.org/10.1007/s40815-021-01188-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-021-01188-6

Keywords