ai-fml international academy ai人機共學國際學院

AI-FML International Academy

AI人機共學國際學院Supported by

International Fuzzy Systems Association (IFSA)

IEEE CIS Task Force on Fuzzy Systems for Web Intelligence

IEEE CIS Task Force on Competitions

IEEE CIS Task Force on Fuzzy Systems Software

Chang-Shing Lee

National University of Tainan, Taiwan

Dec. 28, 2019

• Goal– Promote IEEE 1855-2016 Standard FML.

– Propose FML tools for real-world applications.

– Propose FML learning curriculums for high-

school students and learners.

– Promote high-school students around the world

to combine FML tools with machine learning for

real-world AI applications.

– Apply FML to real-world robotic applications.

– Apply FML to AIOT Applications

– AI-FML as a Service, AI-FaaS/ 541


Current candidate places for this event are as follows:– @ Taiwan: Tainan, Kaohsiung, …

– @ Japan: Tokyo, Osaka, …

– @ Italy: Napoli, …

– @ Canada: Edmonton, …

– @ Spain: Santiago de Compostela, Cordoba, Granada, …

– @ France: Paris, …

– @ UK: Nottingham, …

– …

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• Organizing Committee

– President: Hung-Duen Yang, Taiwan

– Vice Presidents for AI-FML Tools

– Vice Presidents for AI-FML Applications

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Chang-Shing Lee

TaiwanMarek Reformat

Canada

Giovanni Acampora

Italy

Jose M. Alonso

Spain

Toru Yamaguchi

Japan

Po-Hsun Cheng

Taiwan

Yusuke Nojima

Japan

Naoyuki Kubota

Japan

Marie-Jeanne Lesot

France

Amir Pourabdollah

UK


Jose Manuel Soto Hidalgo

Spain

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AI-FML Applications

Co-Learning on Game of Go

Co-Learning on LanguageCo-Learning on Math


Chang-Shing Lee

National University of Tainan, Taiwan

WebDuino / Kebbi Air AI Robot

2019/12/28

AI-FML for AIoT Applications


Robotic Applications (Kebbi)

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http://www.uco.es/JFML/

This library offers a complete implementation of the four FLS enclosed in the W3C XML Schema definition (XSD) of the standard IEEE 1855 for FML:

Mamdani Takagi-Sugeno-Kang (TSK)

JFML: the first Java Library complying with IEEE 1855 Standard for FML

Tsukamoto AnYa

JFML makes use of the new Java API JAXB (Java Architecture for XML Binding) to bind W3C XML

schemas and Java representations, making it easy for Java developers to incorporate XML data and

processing functions in Java applications

JFML includes classes and methods in order to facilitate developers to import/export FLS defined with

• The standard IEC 61131-7

• PMML

• Matlab

JFML is now also accessible in Python 3.x through Py4JFML

https://www.gnu.org/licenses/quick-guide-gplv3.html

Fuzzy-as-a-Service (FaaS)

Problem: Fuzzy Logic Systems are usually associated with dedicated hardware/software.

Stand-alone or simple-networked, desktop computing or embedded

Bottleneck: Complex FLS computations, particularly for ambient devices

Solution: Service-Oriented Architecture (SOA)

Abstraction of processing logic from presentation and data

Openness, reusability, elasticity and performance

FML as data exchange language

JFML as processing logic

Pourabdollah, A., Wagner, C., Acampora, G. and Lotfi, A., 2018, July. Fuzzy Logic As-a-

Service for Ambient Intelligence Environments. In 2018 IEEE International Conference

on Fuzzy Systems (FUZZ-IEEE) (pp. 1-7). IEEE.

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https://ieeexplore.ieee.org/abstract/document/8491522

Human Monitoring System using FML/JFML

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Contacts

Nottingham Trent University, UK.

Dr Amir Pourabdollah

[email protected]

Prof. Ahmad Lotfi

[email protected]

Bhavesh Pandya

[email protected]

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mailto:[email protected]



Web:Bit AI-FML Blocks/ AIoT

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• By surfing on the web, you will find a

template of blocks below.


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This is the ID of the robot Kebbi.

Define your topic name

based on MQTT protocol

Execute the actions that you want a robot Kebbi to do

using blocks provided by Web:Bit


• The followings are available blocks on the Web:Bit website

• FML-related blocks include logic (邏輯), math (數學), expansion

(擴充功能), and Kebbi (凱比機器人)

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Web:Bit Logic Blocks/ AIoT

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• Select block logic(邏輯)

If(如果)…Do(執行)

• Pull block “If …Do”

into the blocks


• Click on a gear icon.

Some blocks will be appeared.

• Select block elseif (否則如果) and move it to the

place below block “if (如果)”.

• A new block will be added.

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• Take Tips Payment for an example. There

are 3 output fuzzy variables.

• Choose blocks elseif (否則如果) and if (否則)

and move them to suitable places.

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低高中

elseif

else


• Select the second block from block logic (邏輯)

• Pull it into the block,

click on the drop-down menu, and

choose icon “<”

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Web:Bit Expansion Blocks/ AIoT

• Select block expansion(擴充功能), broadcast(網路廣播), and then received broadcast(收到的廣播訊息)

• Pull it into block

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Broadcast (網路廣播)

Web:Bit Math Blocks/ AIoT

• Select the first block from block math(數學)

• Pull it into block

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Received broadcast message (收到的廣播訊息)


• The first 「if」(如果) denotes “< 3.5” is 低(low).

• Fill 3.5 in block math (數學).

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3.5

FML knowledge base

& rule base


• The second block 「else if」(否則如果)

denotes that “<6.5” is 中(medium).

• Repeat previous action and fill 6.5 in block

math (數學).

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6.5

FML knowledge base

& rule base


• We design FML Kebbi using blocks do, else if,

and else.

• Block expansion(擴充功能) for robot Kebbi

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• In the first block “do”, we use specific Kebbi

blocks including blocks light (燈光), voice (聲音),

and animation (動畫表演), to control the robot

to action when the output fuzzy variable is

low(低).

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• In the second block “do” , we use blocks light, voice

and animation to control the robot to action when

the output fuzzy variable is medium(中).

• In the second block “else” , we use blocks light,

voice and animation to control the robot to action

when the output fuzzy variable is high(高)

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Web:Bit AI-FML Robot/ AIoT

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AI-FML Robot

Web:Bit AI-FML Robot/ AIoT

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AI-FML Robot

Appendix I

Real-World Applications


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1. Diet/ Healthcare/ Travel• (1) Adaptive personalized diet linguistic recommendation mechanism based on type-2 fuzzy sets

and genetic fuzzy markup language, IEEE Trans. on Fuzzy Systems, vol. 23, no. 5, pp. 1777-1802,

2015.

• (2) Healthy diet assessment mechanism based on fuzzy markup language for Japanese food, Soft

Computing, vol. 20, no 1, pp 359-376, 2016.

• (3) A novel genetic fuzzy markup language and its application to healthy diet assessment,

International Journal of Uncertainty, Fuzziness, and Knowledge-Based Systems, vol. 20, no. 2, pp.

247-278, 2012.

• (4) Evaluating cardiac health through semantic soft computing techniques, Soft Computing, vol.16,

no. 7, pp. 1165-1181, 2012.

• (5) Diet assessment based on type-2 fuzzy ontology and fuzzy markup language, International

Journal of Intelligent System, vol. 25, no. 12, pp. 1187-1216, 2010.

• (6) Ontology-based multi-agents for intelligent healthcare applications, Journal of Ambient

Intelligence and Humanized Computing, vol. 1, no. 2, pp. 111-131, 2010.


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2. E-Learning/ Education/ IRT/ Ontology Construction• (1) Performance Verification Mechanism for Adaptive Assessment e-Platform and e-Navigation

Application, International Journal of e-Navigation and Maritime Economy, vol. 2, pp. 47-62, 2015.

• (2) T2FS-based adaptive linguistic assessment system for semantic analysis and human

performance evaluation on game of Go, IEEE Trans. on Fuzzy Systems, vol. 23, no. 2, pp. 400-

420, 2015.

3. Game/ Go• (1) T2FS-based adaptive linguistic assessment system for semantic analysis and human

performance evaluation on game of Go, IEEE Trans. on Fuzzy Systems, vol. 23, no. 2, pp.

400-420, 2015.

• (2) Soft-Computing-based emotional expression mechanism for game of Computer Go,

Soft Computing, vol. 17, no. 7, pp. 1263-1282, 2013.

• (3) Genetic fuzzy markup language for game of NoGo, Knowledge-Based Systems, vol. 34,

pp. 64- 80, 2012.

• (4) An ontology-based fuzzy inference system for computer Go applications, International

Journal of Fuzzy Systems, vol. 12, no. 2, pp. 103-115, 2010.


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4. Energy Management • (1) An optimization model for FML-based decision support system on energy management,

in Proceeding of 2014 IEEE International Conference on Fuzzy Systems, Beijing, China, 6-11, 2014, pp. 850-856.

• (2) FML-based decision support system for solar energy supply and demand analysis, 2013 IEEE International Conference on Fuzzy Systems, Hyderabad, India, 7-10, 2013.

5. Patent Evaluation• (1) Fuzzy markup language with genetic learning mechanism for invention patent quality

evaluation, in Proceeding of 2015 IEEE Congress on Evolutionary Computation, Sendai, Japan, 25-28, 2015, pp. 251-258.

6. Information Security• (1) IT2FS-based ontology with soft-computing mechanism for malware behavior analysis, Soft

Computing, vol. 18, no. 2, pp. 267-284, 2014.

7. University Assessment• (1) Apply fuzzy ontology and FML to knowledge extraction for university governance and

management, Journal of Ambient Intelligence and Humanized Computing, vol. 4, no. 4, pp. 493-513, 2013.


Appendix II

FML-based Machine Learning

Competition


Competition @ FUZZ-IEEE 2019FML-based Machine Learning Competition

for Human and Smart Machine Co-Learning

on Game of Go

Chang-Shing lee, Yusuke Nojima, Naoyuki Kubota

Giovanni Acampora, Marek Reformat, and Ryosuke Saga

Presenter: Chang-Shing Lee

July 16, 2019

Outline

• Organizers

• Scope and Topic

• Competition Data

• Metrics and Rules

• Evaluation

• Results

• Next Steps: AI-FML International Academy

34 / 54

Organizers

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• Organizers and Poster

• Competition Websitehttp://oase.nutn.edu.tw/fuzz2019-fmlcompetition/

http://oase.nutn.edu.tw/fuzz2019-fmlcompetition/

Outline

• Organizers

• Scope and Topic



• Evaluation

• Results

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Scope and Topic

• Use the FML tool to construct the knowledge base (KB) and

rule base (RB) of the fuzzy inference system.

• Use the data predicted by Facebook AI Research (FAIR)

Darkforest AI Bot as the training data.

• Use the data predicted by FAIR ELF OpenGo as the

Desired Output for the training data and the testing data.

• Optimize the FML KB and RB through the methodologies of

evolutionary computation and machine learning in order to

develop a regression model based on FML-based fuzzy

inference system.

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1855-2016IEEE Standard for Fuzzy Markup Language

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Outline

• Organizers

• Scope and Topic



• Evaluation

• Results

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Competition Data-Google AlphaGo Master 60 Games

• Google AlphaGo Master Series

– Google AlphaGo Master in Dec. 2016/ Jan. 2017

– 60 online fast time-control games with top

professional Go players.

– AlphaGo Master won 60 games.

– Google DeepMind Website:

https://deepmind.com/research/alphago/match-

archive/master/

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

Competition Data-Google AlphaGo Master 60 Games

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Competition Data-FAIR ELF OpenGo AI bot Prediction

FAIR AI bot

Prediction Data

Game 1

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1. DBSN, DWSN, DBWR, DWWR, DBTMR, and DWTMR

were predicted by FAIR Darkforest AI bot.

2. EBWR and EWWR were predicted by ELF OpenGo AI bot.

3. WR: Win Rate; SN: MCTS Simulation Number.

Competition Data

• Training Data: Game 1 to Game 45

– Input: MCTS Simulation Number (DBSN, DWSN), Win

Rate (DBWR, DWWR), and Top-Move Rate (DBTMR,

DWTMR) predicted by Darkforest (NUTN, Taiwan/OPU,

Japan).

– Desired Output: Win Rate (EBWR, EWWR) predicted by

ELF OpenGo (NUTN, Taiwan/OPU, Japan).

• Testing Data: Game 46 to Game 60

– Examine the generalization ability of the learned FML-

based fuzzy inference system.

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Outline

• Organizers

• Scope and Topic



• Evaluation

• Results

44 / 54

Metrics and Rules

• Mean Square Error (MSE)

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Outline

• Organizers

• Scope and Topic



• Evaluation

• Results

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Evaluation

• FML KB and RB: 30%

• Learned KB and RB, Training Data Accuracy,

Testing Data Accuracy: 35%

• Evolutionary Computation & Machine

Learning PPT Slide: 35%

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Outline

• Organizers

• Scope and Topic



• Evaluation

• Results

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Entries and Winners

• 14 entries registration

• 7 entries finish the competition

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Video Presentation

• Video Presentation on Competition Website– Team CILAB_OPU (Osaka Prefecture University, Japan)

– Team OASEWIFI (National University of Tainan, Taiwan)

– Team TMU_Y&K (Tokyo Metropolitan University, Japan)

– Team WeLiveonTop (National Tainan First Senior High School/National Fengshan Senior High School, Taiwan)

– Team NKNU SE+ (National Kaohsiung Normal University, Taiwan)

– Team Milos (National Tainan First Senior High School, Taiwan)

– Team Taiwan Fish (Tsoying Senior High School, Taiwan)

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http://oase.nutn.edu.tw/fuzz2019-fmlcompetition/teamcilabopu.php

http://oase.nutn.edu.tw/fuzz2019-fmlcompetition/teamoasewifi.php

http://oase.nutn.edu.tw/fuzz2019-fmlcompetition/teamtmuyk.php

http://oase.nutn.edu.tw/fuzz2019-fmlcompetition/teamweliveontop.php

http://oase.nutn.edu.tw/fuzz2019-fmlcompetition/teamnknuse.php

http://oase.nutn.edu.tw/fuzz2019-fmlcompetition/teammilos.php

http://oase.nutn.edu.tw/fuzz2019-fmlcompetition/teamtaiwanfish.php

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A Series of FML-based Machine Learning

Competition Activities and Promotion in

Taiwan from 2018 to 2019

• Website https://youtu.be/f7BfQA-FMRQ

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https://youtu.be/f7BfQA-FMRQ

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ai-fml international academy ai人機共學國際學院

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