Organizers: Chang-Shing Lee, Yusuke Nojima, Naoyuki Kubota, Giovanni Acampora, Marek Reformat, and Ryosuke Saga
Supported by Task Forces on Competitions of IEEE CIS Fuzzy Systems Technical Committee
Scope and Topics
- 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.
- Use the data predicted by Facebook AI Research (FAIR) Open Source Darkforest AI Bot as the training data.
- Use the data predicted by Facebook AI Research (FAIR) Open Source ELF OpenGo AI Bot as the desired output of the training data.
- 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.
The participants are invited to submit their results via the competition website (http://oase.nutn.edu.tw/fuzz2019-fmlcompetition/). Participants are also encouraged to submit the results to the competition held in IEEE CEC 2019 (http://oase.nutn.edu.tw/cec2019-fmlcompetition/). We will announce the winner at both conferences.
Submissions must be received before May 10th 2019, 23:59 (GMT).
- FUZZ-IEEE 2019 will provide a certificate of participation to all contestants and award a special certificate to the competition winners
- Cash prizes will be provided to the top three contestants, if the number of contestants exceeds 10 teams. The cash prizes will be 500USD, 300USD, and 200USD, respectively.
- Participants are expected to apply for travel funds from the CIS-IEEE and attend the FUZZ-IEEE 2019 conference, where they will present their results.