FML-based Machine Learning Competition for Human Prediction and Application on Game of Go @ FUZZ-IEEE 2017

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Overview of Competition
With the recent 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 vs. computer programs or computer programs against each other. While computer programs are already better than humans (even high level professionals), machine learning still offers interesting prospects, both from the fundamental point of view (1) to even further 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 of enhancing the human playing experience by coaching professionals to play better or training beginners. The latter problem raises interesting questions of explainability of machine game play. This competition will evaluate the potential of learning machines to teach humans.

Previous human vs. machine Go playing competitions have focused on having machines compete with humans. This competition presents both this aspect and the new aspect of having machines and humans collaboration. In this competition students and researchers (ML competitors) will propose new machine learning techniques or apply existing one to create programs that play Go and/or teach humans to play, or suggest better moves. Interestingly, several high-end systems have now been made available as open-source, making it possible to build Go teaching systems on top of existing state-of-the-art game playing technology. We will invite pre-selected humans and machines (Go competitors) to participate in a high-end Go tournament. For this first event, we intend to invite professional Go players and select proven computer Go systems. The professional Go players will evaluate the pedagogical capabilities of the programs designed by the ML competitors to provide good guidance on how to play. To participate in the live competition that we propose the ML competitors will be pre-selected with the DyNaDF Platform ( that we used in previous challenges. To simplify the task and make it possible for students to contribute, we will allow them to contribute a post-processing module building on top of an existing structure. This structure involves 3 stages: Stage 1 provides prediction results of the Darkforest Go engine (Facebook's deep learning Go player), stage 2 infers results of the knowledge-based engine (based on the FML IEEE standard), and stage 3 combines the ML competitor model with the two previous stages to predict the possible winner of the game. We will supply training and test data taken from 60 games from Google Master vs. top professional Go players in Dec. 2016 and in Jan. 2017. The final stage of our system (Stage 4) will include a robot engine, which can speak and explain in real-time the situation to Go players. To that end the ML competitors will have to supply an explanation in text of the proposed (best) moves. For pre-selection, the students should show that their proposed approach is viable and results in reasonable time.
Details about the competition, please refer to the website:


Potential Humans
- Chun-Hsun Chou (9P / Taiwan)
- Lu-An Li (6D / Taiwan)
- Shuji Takemura (1D / Japan)
- Minoru Ueda (5K / Japan)

Potential Computer Go Programs
- ZEN is developed by Yoji Ojima and Hideki Kato, Japan.
- CGI is developed by CGI Lab, NCTU, Taiwan.
- Darkforest Open Source is developed by Yuandong Tian (FAIR, Facebook, USA) and was open in 2016.

Chairs / Co-Chairs / Committee Members
- Chang-Shing Lee, Taiwan / Naoyuki Kubota, Japan / Giovanni Acampora, Italy
- Yusuke Nojima, Japan / Marek Reformat, Canada / Jialin Liu, UK
- Ryosuke Saga, Japan / Fabien Teytaud, France / Autilia Vitiello, Italy / Cyril Fonlupt, France