Competition

FML-Based Machine Learning Competition for Human and Smart Machine Co-Learning on Game of Go

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
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. While computer programs are already better than human players (even high-level professionals), machine learning still offers interesting prospects, both from the fundamental point of view 1) to even further know the limits of game playing (having programs playing against each other), 2) to better understand machine intelligence and compare it to human intelligence, and from the practical point of view 3) to enhance the human playing experience by coaching professionals to play better or training beginners. The latter prospect also raises interesting questions of the explainability of machine game play. This competition will evaluate the potential of learning machines to teach human players.
The goal of this competition includes:
The OpenGo Darkforest (OGD) Cloud Platform for Game of Go includes the following parts:
  • 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.
Submission Instructions

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.

Submission Deadline

Submissions must be received before May 10th 2019, 23:59 (GMT).

Additional Details
  • 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.