Overview

Aims

The proposed summer school, “Quantum Computational Intelligence for Student Experience with Generative AI Applications,” aims to engage pre-university and undergraduate students in foundational and applied topics of Quantum CI, including fuzzy systems, evolutionary computation, and generative AI. The objectives are to 1) attract more young participants to IEEE CIS conferences and competitions, 2) expand outreach and educational opportunities to high school and undergraduate students, and 3) provide engaging STEM-based learning through concept-based lectures, hands-on activities, and team competitions This summer school proposal is highly relevant to the IEEE Computational Intelligence Society (CIS) as it directly supports the society’s mission to promote the theory, design, application, and development of computational intelligence (CI) systems. The event introduces pre-university and undergraduate students to foundational and advanced topics in Quantum Computational Intelligence (QCI), including fuzzy systems, evolutionary computation, and generative AI. By incorporating both concept-based and hands-on learning sessions using the QCI&AI software and hardware, the program fosters early engagement in CI research and applications. It also aligns with IEEE CIS’s goals of global outreach and education by involving students and volunteers from multiple countries, encouraging international collaboration, and inspiring future contributions to CIS conferences, research, and membership. Through these activities, the summer school serves as a bridge between young learners and the broader CI community, cultivating the next generation of researchers and practitioners in the field. This model has been successfully implemented at events including IEEE CEC 2023 (USA), FUZZ-IEEE 2023 (Korea), IEEE WCCI 2024 (Japan), NTNU & IEEE SSCI 2025 (Norway), and IEEE R10 SPNIC Activities (2023–2025) in Taiwan, Malaysia, Japan, and Hong Kong, consistently drawing global student participation and strengthening the IEEE CIS educational network.

Venue and Dates

Venue Website (Reims Convention Center, Reims, France)
Dates: July 4−July 6
Duration: 3 days

Technical Co-sponsors

IEEE CIS High-School Outreach Subcommittee
IEEE CIS ETTC Task Force on Quantum Computational Intelligence
KWS Center, National University of Tainan, Taiwan (http://kws.nutn.edu.tw)
Computational Intelligence Lab., Osaka Metropolitan University, Japan

Supporters

IEEE CIS Taipei Chapter
IEEE Taipei Section
National University of Tainan, Taiwan

Registration Deadline

Registration must be received before June 3, 2025 via the competition website (Register from).

Available Software Tools

VisualFMLTool : It can be executed on platforms containing the Java Runtime Environment. The Java Software Development Kit, including JRE, compiler and many other tools can be found at here. The VisualFMLTool can download from here and then to extract it. Then it is only needed to click the file VisualFMLTool.bat included in the zip to execute the tool.
QCI&AI-FML Learning Platform : It is developed by KWS center/OASE Lab., NUTN, Taiwan and can be executed on different platforms online. After registering the competition, we can provide an account for the participants.
ZAI-FML Learning Platform : It is developed by Zsystem Co. Ltd., Taiwan and can be executed on different platforms online. The participants can apply for a trial account online.
JFML : A spanish research group (Jose Manuel Soto Hidalgo, Giovanni Acampora, Jesus Alcala Fernandez, Jose Alonso Moral) has released a library for FML programming that is very simple to use and compliant with IEEE 1855. JFML can download from here. Additional information about the library is here.
Some References associated to JFML
- J. M. Soto-Hidalgo, Jose M. Alonso, G. Acampora, and J. Alcala-Fdez, "JFML: A Java library to design fuzzy logic systems according to the IEEE Std 1855-2016," IEEE Access, vol. 6, pp. 54952-54964, 2018.
- J. M. Soto-Hidalgo, A. Vitiello, J. M. Alonso, G. Acampora, J. Alcala-Fdez, "Design of fuzzy controllers for embedded systems with JFML," International Journal of Computational Intelligence Systems, vol. 12, no. 1, pp. 204-214, 2019.

Reference

C. S. Lee, M. H. Wang, R. P. Chang, H. C. Liu, S. C. Chiu, Y. C. Chang, L. A. Lin, and S. C. Chen, "Computational intelligence & AI-FML experience model for pre-university student learning and practice," Proceeding of the 18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP 2022), vol. 2, Kitakyushu, Japan, Dec. 16-18, 2022. (ISBN: 978-981-99-0104-3)
C. S. Lee, M. H. Wang, Y. L. Tsai, L. W. Ko, B. Y. Tsai, P. H. Hung, L. A. Lin, and N. Kubota, "Intelligent agent for real-world applications on robotic edutainment and humanized co-learning," Journal of Ambient Intelligence and Humanized Computing, 2019.
C. S. Lee, M. H. Wang, L. W. Ko, Y. Hsiu Lee, H. Ohashi, N. Kubota, Y. Nojima, and S. F. Su, "Human intelligence meets smart machine: a special event at the IEEE International Conference on Systems, Man, and Cybernetics 2018," IEEE Systems, Man, and Cybernetics Magazine, vol. 6, no. 1, pp. 23-31, Jan. 2020.
C. S. Lee, M. H. Wang, L. W. Ko, N. Kubota, L. A. Lin, S. Kitaoka, Y. T Wang, and S. F. Su, "Human and smart machine co-learning: brain-computer interaction at the 2017 IEEE International Conference on Systems, Man, and Cybernetics," IEEE Systems, Man, and Cybernetics Magazine, vol. 4, no. 2, pp. 6-13, Apr. 2018.
C. S. Lee, M. H. Wang, S. C. Yang, P. H. Hung, S. W. Lin, N. Shuo, N. Kubota, C. H. Chou, P. C. Chou, and C. H. Kao, "FML-based dynamic assessment agent for human-machine cooperative system on game of Go," International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 25, no. 5, pp. 677-705, 2017.
G. Acampora, "Fuzzy Markup Language: A XML based language for enabling full interoperability in fuzzy systems design,” in G. Acampora, V. Loia, C. S. Lee, and M. H. Wang (editors)," On the Power of Fuzzy Markup Language, Springer-Verlag, Germany, Jan. 2013, pp. 17–33.
IEEE Standards Association, IEEE Standard for Fuzzy Markup Language, Std. 1855-2016, May 2016. [Online] Available: https://ieeexplore.ieee.org/document/7479441.
G. Acampora, B. N. Di Stefano, A. Vitiello, "IEEE 1855TM: The first IEEE standard sponsored by IEEE Computational Intelligence Society," IEEE Computational Intelligence Magazine, vol. 11, no. 4, pp. 4–6, 2016.
J. M. Soto-Hidalgo, J. M. Alonso, and J. Alcalá-Fdez, "Java Fuzzy Markup Language," Jan. 2019. [Online] Available: http://www.uco.es/JFML/.
Y. Tian and Y. Zhu, "Better computer Go player with neural network and long-term prediction," 2016 International Conference on Learning Representations (ICLR 2016), San Juan, Puerto Rico, May 2–4, 2016. https://arxiv.org/pdf/1511.06410.pdf
Y. Tian and L. Zitnick, "Facebook Open Sources ELF OpengGo," May 2018, [Online] Available: https://research.fb.com/facebook-open-sources-elf-opengo/.
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel and D. Hassabis, "Mastering the game of Go with deep neural networks and tree search," Nature, no. 529, pp. 484–489, 2016.
D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, Y. Chen, T. Lillicrap, F. Hui, L. Sifre, G. v. d. Driessche, T. Graepel, and D. Hassabis, "Mastering the game of Go without human knowledge," Nature, vol. 550, pp. 35–359, 2017.
Deepmind, "AlphaGo Master series: 60 online games,” Jan. 2019. [Online] Available: https://deepmind.com/research/alphago/match-archive/master/.
C. S. Lee, M. H. Wang, and S. T. Lan, "Adaptive personalized diet linguistic recommendation mechanism based on type-2 fuzzy sets and genetic fuzzy markup language," IEEE Transactions on Fuzzy Systems, vol. 23, no. 5, pp. 1777-1802, Oct. 2015.
C. S. Lee, M. H. Wang, H. Hagas, Z. W. Chen, S. T. Lan, S. E. Kuo, H. C. Kuo, and H. H. Cheng, "A novel genetic fuzzy markup language and its application to healthy diet assessment," International Journal of Uncertainty, Fuzziness, and Knowledge-Based Systems, vol. 20, no. 2, pp. 247-278, Oct. 2012.
C. S. Lee, M. H. Wang, L. C. Chen, Y. Nojima, T. X. Huang, J. Woo, N. Kubota, E. Sato-Shimokawara, T. Yamaguchi, "A GFML-based robot agent for human and machine cooperative learning on game of Go," in Proceeding of 2019 IEEE Congress on Evolutionary Computation (IEEE CEC 2019), Wellington, New Zealand, Jun. 10-13, 2019, pp. 793-799.
C. S. Lee, Y. L. Tsai, M. H. Wang, W. K. Kuan, Z. H. Ciou, and N. Kubota, "AI-FML agent for robotic game of Go and AIoT real-world co-learning applications," 2020 World Congress on Computational Intelligence (IEEE WCCI 2020), Glasgow, Scotland, UK, Jul. 19-24, 2020.
C. S. Lee, M. H. Wang, Y. Nojima, M. Reformat, and L. Guo, "AI-Fuzzy Markup Language with Computational Intelligence for High-School Student Learning," arXiv, Cornell University, Nov. 2021.
C. S. Lee, M. H. Wang, W. K. Kuan, S. H. Huang, Y. L. Tsai, Z. H. Ciou, C. K. Yang, and N. Kubota, "BCI-based hit-loop agent for human and AI robot co-learning with AIoT application," Journal of Ambient Intelligence and Humanized Computing, vol. 14, pp. 3583–3607, Oct. 2021
C. S. Lee, Y. L. Tsai, M. H. Wang, S. H. Huang, M. Reformat, and N. Kubota, "Adaptive fuzzy neural agent for human and machine co-learning," International Journal of Fuzzy Systems, vol. 24, pp. 778–798, Nov. 2021.
C. S. Lee, M. H. Wang, Z. H. Ciou, R. P. Chang, C. H. Tsai, S. C. Chen, T. X. Huang, E. Sato-Shimokawara, and T. Yamaguchi, "Robotic assistant agent for student and machine co-learning on AI-FML practice with AIoT application," 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2021), Luxembourg, Luxembourg, Jul. 11-14, 2021.
C. S. Lee, M. H. Wang, M. Reformat, S. H. Huang, "Human intelligence-based Metaverse for co-learning of students and smart machines," Journal of Ambient Intelligence and Humanized Computing, 2023.
C. S. Lee, M. H. Wang, S. H. Huang, F. J. Yang, C. H. Tsai, and L. Q. Wang, "Fuzzy ontology-based intelligent agent for high-school student learning in AI-FML Metaverse," 2022 IEEE World Congress on Computational Intelligence (IEEE WCCI 2022), Padua, Italy, Jul. 18-23, 2022.
C. S. Lee, M. H. Wang, R. P. Chang, H. C. Liu, S. C. Chiu, Y. C. Chang, L. A. Lin, and S. C. Chen, "Computational intelligence and AI-FML experience model for pre-university student learning and practice," The 18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP 2022), Kitakyushu, Japan, Dec. 16-18, 2022.
C. S. Lee, M. H. Wang, C. Y. Chen, M. Reformat, Y. Nojima, and N. Kubota, "Knowledge graph-based genetic fuzzy agent for human intelligence and machine co-learning," 2023 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2023), Songdo International City, Korea, Aug. 13-17, 2023. (Accepted).
G. Acampora, R. Schiattarella, and A. Vitiello, "On the implementation of fuzzy inference engines on quantum computers," IEEE Transactions on Fuzzy Systems, vol. 31, no. 5, pp. 1419-1433, 2023.
C. S. Lee, M. H. Wang, M. H. Wang, P. Y. Wu, R. Schiattarella, G. Acampora, and A. Vitiello, "Fuzzy markup language-based quantum FIE for student and robot co-learning model assessment," 2023 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2023), Songdo International City, Korea, Aug. 13-17, 2023. (Late Breaking paper)
A. Pourabdollah, C. Wilmott, R. Schiattarella, and G. Acampora, "Fuzzy inference on quantum annealers," 2023 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2023), Songdo International City, Korea, Aug. 13-17, 2023.
G. Acampora, M. Grossi, and R. Schiattarella, "A comparison of quantum computer architectures in running fuzzy inference engines," 2023 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2023), Songdo International City, Korea, Aug. 13-17, 2023.
G. Acampora, A. Massa, R. Schiattarella, and A. Vitiello, "Distributing fuzzy inference engines on quantum computers," 2023 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2023), Songdo International City, Korea, Aug. 13-17, 2023.

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