活動說明

• 活動內容目的 

⭐吸引更多年輕參與者參加IEEE CIS國際會議和競賽

⭐透過IEEE國際學術組織推廣大學生和中小學生對量子計算智慧未來應用

⭐辦理STEM活動競賽來鼓勵年輕學生，包括中小學生和大學生積極參與全國AI專題競賽

• Quantum CI體驗應用示範競賽已在2023年至2024年，分別於美國芝加哥的IEEE CEC 2023、韓國仁川的FUZZ-IEEE 2023 、國立臺南大學、日本東京都立大學、馬來西亞檳城、香港城市大學、 香港理工大學與日本橫濱IEEE WCCI 2024 國際會議，辦理量子計算智慧體驗與實作工作坊與場域驗證。 

• 參與學生將在實際生活應用情境中使用由IEEE R10及國立臺南大學理工學院提供的QCI&AI-FML學習工具(硬體/軟體)來設計QCI&AI-FML機器人以完成各種任務。

🥇 第一名 : 20,000元

🥈 第二名 : 10,000元

🥉 第三名 : 5,000元

🏅 特優(三隊) : 2,000元/隊

🏅 佳作(五隊) : 1,000元/隊

人機共學採用四階段與六步驟的方式進行，簡要說明如下:

四階段包括: 

1. 學習資料課前準備及收集階段

2. 學習資料前處理階段

3. 學習資料分析階段

4. 學習資料評估階段

資料收集階段包括六步驟: 

• 觀察與參與計算智慧(Computational Intelligence, CI)學習活動

• 學習與研究CI知識

• 利用與遵循CI學習工具

• 理解與知道CI生活應用

• 解釋與講述CI知識概念

• 簡要說明如下:

• 步驟1: 教學者觀察學習者能力狀況，並準備相對應CI教學內容後，前往教學現場; 

• 步驟2及步驟3: 在教學場域教學者與學習者共同體驗學習CI教學內容，進行CI概念式學習，並進一步深入研究相對應CI內容; 

• 步驟4: 學習者經過CI學習之後，可以利用與遵循進行CI體驗式學習; 

• 步驟5: 經過CI體驗式學習之後，學習者理解與知道CI原理，並可以進行CI實作式學習與CI操作式學習; 

• 步驟6: 學習者可以將CI學習內容進行解釋，並講述給其它學習者了解，進行CI表達式學習。

• VisualFMLTool : 它可以在包含Java運行時環境（JRE）的平台上執行。Java軟體開發工具包（SDK），包括JRE、編譯器和許多其他工具 ，可以在此處here。VisualFMLTool可以從此處here下載，然後進行解壓縮。之後只需點擊壓縮包中的 VisualFMLTool.bat 文件即可執行該工具。 

• QCI&AI-FML 學習平台 : 它由國立台南大學的KWS中心/OASE實驗室開發，可在線上不同平台上執行。註冊參加競賽後，我們將為參與者提供一個帳號。 

• ZAI-FML 學習平台 : 它由台灣的Zsystem有限公司開發，可在不同的線上平台上執行。參與者可以在線申請試用帳號。 

• JFML : 一個西班牙研究小組（包括Jose Manuel Soto Hidalgo、Giovanni Acampora、Jesus Alcala Fernandez、Jose Alonso Moral）釋出了一個用於FML編程的庫，該庫非常易於使用並符合IEEE 1855標準。JFML可以從此處here下載，有關該庫的更多信息請參見此處here。

• 以下是與JFML相關的一些參考文獻 

  • 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.

• 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.