活動說明

活動目標

了解FML模糊推論系統與機器學習基本概念

使用FML智慧決策工具建置模糊推論系統之知識庫與規則庫

實作FML模糊推論系統實務應用

透過機器學習方法優化FML知識庫與規則庫

活動時間

2020年8月21日至8月23日

活動說明

第一名獎金3000元

第二名獎金2000元

第三名獎金1000元

佳作獎金500元

自選題目

健康飲食評估

智慧旅遊規劃

學習成效評估

智慧生活應用

評比方式

 1) 機器學習前之FML知識庫與規則庫、機器學習後之FML知識庫與規則庫
訓練資料、測試資料、資料處理過程與機器學習機制等相關說明文件(30%)

 2) 投影片(30%)

 3) 現場報告(30%)

 4) Q&A (10%)

相關軟體

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.

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.

參考文獻

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, 2019. (DOI: 10.1109/MSMC.2019.2948050)

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. arXiv

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. [Oneline] 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.