Overview

Scope and Topic

The goal of this competition is to optimize the FML knowledge base and rule base through the methodologies of evolutionary computation and machine learning in order to develop a regression model based on FML-based fuzzy inference system and apply it to prediction models of the winning rates for Alpha Go Master Series or for real-world applications.
In addition to game of Go, the topic of the competition includes AIoT applications or real-world applications to encourage elementary-school students, high-school students, or undergraduate students in the world to join the competition for education and learning on fuzzy logic and system.

With the success of AlphaGo, there has been a lot of interest among students and professionals to apply machine learning to gaming and in particular to the game of Go. Several conferences have held competitions human players vs. computer programs or computer programs against each other. The goal of this competition includes:
Understand the basic concepts of an FML-based fuzzy inference system.
Use the FML intelligent decision tool to establish the knowledge base and rule base of the fuzzy inference system.
Optimize the FML knowledge base and rule base through the methodologies of evolutionary computation and machine learning in order to develop a regression model based on FML-based fuzzy inference system.
poster
 

Category A: Winning Rate Prediction for Alpha Go Master Series

The participants construct the prediction models of the winning rates for Alpha Go Master Series by using FML.
Please refer https://github.com/CI-labo-OPU/FML_Competition2020. The goal, datasets, and regulations are the same as 2020. We are preparing 2021 version but the contents would be almost the same as 2020.

 

Category B: Real-World Applications

The participates construct the knowledge base and the rule base of the FML-based inference system with machine learning tools for real-world applications.
 

Category C: AIoT Applications

The participates construct the knowledge base and the rule base of the FML-based inference system with machine learning tools for real-world applications. In addition, the system can connect to the AIoT devices, for example, using JFML and Raspberry Pi or arduino.
 

Submission Instructions

The participants are invited to submit their results via the competition website (http://oase.nutn.edu.tw/fuzz2021-fmlcompetition).
 

Submission Deadline

June 30, 2021 (Extended)
 

Evaluation for Group University/College

The competition will be done before the conference. They should construct the FML system and write system description document with 2 or 3 pages. The participates must submit the following files using a ZIP file after login the competition website. The PowerPoint template is here.
Part 1: KB/RB (30%)
     1) their original knowledge base and rule base described by FML and related files to explain them (15%), and
     2) their learned knowledge base and rule base described by FML, training data accuracy, testing data accuracy, and related files to explain them (15%),
Part 2: Documentation (30%)
     1) a slide to explain them (15%), and
     2) a pdf file to explain them (15%).
Part 3: Video Presentation (30%)
    1) a 5-min Presentation on YouTube (15%), and
    2) a 10-min Presentation on YouTube (15%).
Part 4: Online Q/A  (10%)
 

Evaluation for Group Senior High School / Group Junior High School / Group Elementary School

The competition will be done before the conference. They should construct the FML system and write system description document with 2 or 3 pages. The participates must submit the following files using a ZIP file after login the competition website. The PowerPoint template is here.
Part 1: KB/RB (30%)
     1) their original knowledge base and rule base described by FML and related files to explain them (15%), and
     2) their learned knowledge base and rule base described by FML, training data accuracy, testing data accuracy, and related files to explain them (15%),
Part 2: Documentation (30%)
     a slide to explain them (30%)
Part 3: Video Presentation (30%)
    a 5-min or 10-min Presentation on YouTube (30%)
Part 4: Online Q/A  (10%)
 

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.
AI-FML Tool : 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.
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

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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.
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