# Overview

## Scope and Topic

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.

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.

## Competition Data

AlphaGo Master series: 60 online games Master in Dec. 2016 and in Jan. Over one week, AlphaGo played 60 online fast time-control games. AlphaGo won this series of games 60 – 0.

Website: https://deepmind.com/research/alphago/match-archive/master/

Each GameData file includes the prediction by Darkforest AI Bot and EFL OpenGo AI Bot for one game.

MoveNo is the move number. MoveNo only lists "odd" numbers (i.e., 1, 3, 5, ...) because each row corresponds to a pair of one Black move and one White move. That is, the row with the MoveNo 1 corresponds to the Black first move (i.e., B1) and the White first move (i.e., W2). The row with the MoveNo 145 corresponds to the Black 145th move (i.e., B145) and the White 146th move (i.e., W146). If the final MoveNo is "odd", "White's information of the last row" will be vacant.

Each row includes eight values (i.e., DBSN, DWSN, DBWR, DWWR, DBTMR, and DWTMR were the outputs from Darkforest. EBWR and EWWR were the outputs from ELF OpenGo).

DBSN: The number of simulations for Black. DWSN: The number of simulations for White. DBWR: The win rate of Black. DWWR: The win rate of White. DBTMR: The top-move rate of Black. DWTMR: The top-move rate of White.

EBWR: The win rate of Black. EWWR: The win rate of White.

The number of rows represents the length of the game and is different among game data files

The participates can choose any 40 Games from 60 Games as the training data and the remaining 20 Games as the testing data. The inputs are the number of simulations (DBSN, DWSN), the win rates (DBWR, DWWR), and the top-move rates (DBTMR, DWTMR) predicted by Darkforest. The desired outputs are the win rates, (EBWR, EWWR) predicted by ELF OpenGo AI Bot. The participates construct the knowledge base and the rule base of the FML-based inference system. The testind data are used to examine the generalization ability of the learned FML-based inference system. (Download Data of Game 1 to Game 60)

## Submission Instructions

The participants are invited to submit their results via the competition website (http://oase.nutn.edu.tw/wcci2020-fmlcompetition).

## Submission Deadline

April 15, 2020, 23:59 (GMT)

## Metrics and Rules

The proposed regression model is evaluated by the mean squared error over all moves in the test game datasets.

where M is the total number of moves (i.e., the total number of rows) in the test data. xi and yi are the Black's win rate predicted by the regression model and the Black's win rate calculated by ELF OpenGo AI Bot (i.e., EBWR), respectively. Note: EWWR = 1.0 – EBWR.

Only the training data is available for optimizing/learning the regression model and tuning the parameters of optimization/learning algorithms.

## Evaluation

The competition will be done before the conference. We will release the Java-based FML tool and call for applications to construct the knowledge base and rule base of FML. They should construct the FML system and write system description document with 2 or 3 pages. The competition will be held on the Internet. The participates must submit the following files using a ZIP file after login the competition website.

1) their original knowledge base and rule base described by FML and related files to explain them (25%),

2) their learned knowledge base and rule base described by FML, training data accuracy, testing data accuracy, and related files to explain them (30%),

3) their slide (25%), and

4) a pdf file (20%) (template) to explain them.

## Category 1: Prediction of EBWR for the current move

In this category, the goal is to design a fuzzy rule-based regressoin model which can accurately predict EBWR(t) using some or all of the input attributes. The meaning of this category is as follows. Let assume two players A and B. Player A uses Darkforest AI bot to get the hint of the next moves, while Player B uses EFL OpenGo AI bot to do the same. Each player cannot know the suggestion by the opponent's AI bot. So, Player A uses the fuzzy rule-based regression model to guess the current situation which is predicted by the opponent's AI bot. If it is possible to know that, Player A can evaluate the current situation from multiple viewpoints (i.e., Darkforest and EFL OpenGo).

## Category 2: Prediction of DBWR for the next move

In this category, the goal is to design a fuzzy rule-based regressoin model which can accurately predict DBWR(t+1) using some or all of the input attributes. The meaning of this category is as follows. Let assume that Player A wants to know the future situation to change the strategy at a proper moment. To do that, Player A uses the fuzzy rule-based regression model to predict the winning rate for the next move as well.

For more details about Category 2, please click here.

## Fuzzy Markup Language (FML ver. 0.1.1) Introduction and Implementation

For more details about FML, please download the FML user guide from

http://kws.nutn.edu.tw/fmldoc/

## 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.

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.

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