Fuzzy Markup Language for RealWorld Applications(Combined ...

Fuzzy Markup Language for Real-World Applications 國立台南大學資訊工程學系 李健興 2017/03

Outline • WCCI 2016 Tutorial • Applications –Summarization Agent –Classification Agent –Prediction Agent –Demonstration 1

Human vs. Computer Go Competition History

Video

2

/5

Applications Summarization Agent

3

Applications Classification Agent

4

Applications Prediction Agent

5

Applications

Demonstration

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/5

FUZZ-IEEE 2016 Tutorial: FUZZ-IEEE-03 Type-2 Fuzzy Ontology and Fuzzy Markup Language for Real-World Applications Organized by Chang-Shing Lee, National University of Tainan, Taiwan Giovanni Acampora, Nottingham Trent University, UK Yuandong Tian, Facebook AI Research, USA 24 July, 2016 0 / 71

FUZZ-IEEE 2016 Tutorial: FUZZ-IEEE-03 Part 1: Type-2 Fuzzy Ontology and Applications Chang-Shing Lee, NUTN, Taiwan Part 2: Fuzzy Markup Language Giovanni Acampora, NTU, UK Part 3: Real-World Application on Game of Go Yuandong Tian, Facebook AI Research, USA 1 / 71

FUZZ-IEEE 2016 Tutorial: FUZZ-IEEE-03 Part 1 Type-2 Fuzzy Ontology and Applications Chang-Shing Lee National University of Tainan, Taiwan

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Research Team

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Co-Sponsors

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Type-2 Fuzzy Ontology Applications • • • • • •

FML IEEE 1855-2016 Standard Type-2 Fuzzy Set Fuzzy Ontology Game of Go Application Personalized Diet Recommendation Adaptive Learning Application

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FML IEEE 1855-2016

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Introduction to T2FS (1/5)

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Introduction to T2FS (2/5) ~ A

u

( xi )

Vertical Slice

Wx '1

1

Wx ' N

~ UMF ( A)

~ UMF ( A)

u MF1 ( x ) i

u1

MFN ( x ) i

MFN ( x ) i

un

~ A

( x, u ) Embedded T2 FS

x

MF1 ( x i )

u

~ LMF ( A)

0

l Uncertainty About Left End-Point

x

x

i

r

Embedded T1 FS

~ Some eye Contact ( A)

Uncertainty About Right End-Point

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Introduction to T2FS (3/5) u 1

~

UMF ( A)

~

UMF ( A)

Embedded FS

~

LMF ( A)

~

FOU ( A)

~

FOU ( A)

X 9 / 71

Introduction to T2FS (4/5) Type-2 FLS Output Processing Rules

Crisp inputs

Defuzzifier

Type-reducer

Fuzzifier

x X Fuzzy input sets ~

Ax (or Ax )

Inference

Crisp outputs

y Y

Type-reduced Set(Type-1)

Fuzzy output sets ~ Fx

第二型模糊邏輯系統

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Introduction to T2FS (5/5) u

u

~

Low

1.0

Low

1.0

0.8

0.8

0.6

0.6

0.4

0.4

0.2

UMF

LMF

0.2 0 5

10

15

20

25

30

40

0

0

x( C )

5

A

10

15

20

25

30

40

x(0C )

( x , u) 1.0

u ( x , u)

0.8

~ A

0.6

1.0

0.4

0.8

0.2

5

10

15

20

25

30

40

0

0.6 0.4

0.2

x

0.4 0.6

0.2

0.8

0

0.2

0.4

0.6

0.8

1.0

u

1.0

u

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Dynamic Assessment and IRT-based Learning Application

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Video Demonstration • • • •

Taiwan Open 2009 Human vs. Computer Go @ IEEE WCCI 2012 Human vs. Computer Go @ FUZZ-IEEE 2011 Human vs. Computer Go in Taiwan in 2011

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Adaptive linguistic assessment Domain Expert

Game Results Repository

T2FS Construction Mechanism

Human vs. MoGoTW

PSO Model Estimation Mechanism

Adaptive UCT-based Go-Ranking Mechanism Bradley-Terry Model Estimation Mechanism

MoGoTW

Game Results Repository

KB/RB Repository

Domain Expert

Adaptive Go-Ranking Assessment Ontology

T2FS-based Genetic Learning Mechanism

T2FS-based Fuzzy Inference Mechanism

Players Human-Performance Mapping Mechanism

Personal Profile Repository

Semantic Analysis Mechanism

Players Rank Repository

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Go-ranking assessment ontology Adaptive Go-Ranking Assessment Ontology 13x13

7x7

9x9

...

NCKU FUZZ-IEEE 2013

NUTN

...

Certificated Rank 6D

...

Machine Spec HP ProLiant DL785

Komi 7.5

Where

Professional Player

Gender Male

Time Setting 45mins/Side

White

Class Layer

...

Who

Age 45

...

2013/7/8

...

Category Layer

IEEE WCCI 2012

Amateur Player

MoGoTW 2013/7/9

19x19

...

...

God Temple

Domain Layer

...

When How

Rule Chinese

Black

Round2

Round1

...

7.5

.

~ Komi

Game12 SN12,GR12

Game14 SN14,GR14

..

Game13 SN13,GR13

Game11 SN11,GR11

Game1K SN1K,GR1K

~

Game1K-1 SN1K-1,GR1K-1

RoundN

~

WinningRate 60

SN 121145

...

...

~

GameWeight 19

... RankActual 6D

RankMethod 6.38D SN: Simulation Number GR: Game Result

~

Low

~

Medium

What

~

High

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Fuzzy inference structure Note: (1) M denotes number of fired rules (2) x {GW , WR , SN , Komi } (3) GW is GameWeight (4) WR is WinningRate

Rank ( x )

Output Layer

Type-Reduction Layer

AVG

Rank l (x )

Rank r (x )

KM

[ Rank lM , Rank rM ]

[ Rank l1 , Rank r1 ]

...

KM 1

MIN

Antecedent Layer ...

(GW ), Medium

Low

~ (G W )] (G W ), GW Low

[ Komi ~

( Kom i ), Low

~

KomiLow

M

[ f ( x ), f ( x )]

( Kom i )]

[

MIN ~

Komi Medium

( Kom i ),

~

( Kom i )]

~

( Komi )]

Komi Medium

...

~ [ GW

~ [ GW

KM

M

...

1

[ f ( x ), f ( x )]

...

Rule Layer

...

Consequent Layer

KM

~ GW Medium

(GW )]

[

~

GWHigh

(GW ),

~

GWHigh

(GW )]

[

~

KomiHigh

( Komi ),

KomiHigh

Input Layer

SN

GW

WR

Komi

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Personalized Diet Recommendation

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Diet assessment / recommendation ontology Adaptive Diet Assessment Ontology UK

Japan

Taiwan

Domain Layer

...

USA

...

ChiKu Campus

Category Layer

NUTN FuCheng Campus

...

RongYu Campus

...

VCI Lab.

UnderGraduate

OASE Lab.

Assistant

...

11/14/2009

6(6) servings

Meats & Proteins 6(6)servings

~ Fruits 3.5(3.5) servings

~ Dumpling 1.5(1.5) portions

~Corn Soup 1(1)portion 1(1) portion

1(1) portion

1(1) portion Fats & Nuts ~ 9(9.3) servings

Meats & Proteins ~ 10(9.65) servings

Carbohydrate ~ 1197(1196.8)kcal FGB ~ 1(0.66)

~Sugar 72(72)g

~ PCP 16(16.44)%

~

~

Low

~

Medium

How

Low-Fat Milk ~ 1.5(1.5) servings

What Corn Soup

High

Fruits

Caramel

~ Pudding

1(1) portion

...

~0(0) serving

Low-Fat Milk ~ 1(0.6) serving

~ Fat 874(874.35)kcal ~ PCF 35(35.28)%

~ PCR 124(123.93)%

... DHLDO ~ 4(4)

~

~Diet Goal 2000(2000)kcal

~1(1)portion

... DHLMethod ~ 3.4(3.42)

PCC: Percentage of Calories from Carbohydrate PCP: Percentage of Calories from Protein PCF: Percentage of Calories from Fat

Rclass_Semantic VeryLow

When

Vegetables ~ 3.5(3.5)servings

~Vegetables 0.5(0.5) serving

~ Protein 407(407.4)kcal

~ PCC 48(48.29)%

PCR: Percentage of Caloric Ratio FGB: Food Group Balance DHL: Dietary Healthy Level DO: Desired Output

11/30/2009

Seafood Spaghetti with Tomato Sauce ~1(1) portion

~Black Tea

~Soy Milk

~Pork Bun

Who

Dinner

...

Lunch

Where

Advisor

~

Actual Caloric Intake ~ 2500(2500)kcal

Breakfast

Whole Grains & Starches ~ 14.5(14.5) servings

...

~Fats & Nuts

Whole Grains & Starches ~ 12(12) servings

...

CASDL Lab.

Graduate

...

11/1/2009

Class Layer

~

VeryHigh

...

Recommended Semantics Layer

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Personalized diet recommendation Step 6.2

Taiwan Step 5

…… Step 1

Personalized KB

…

Domain Experts Step 2

… Subjects

Step 4

Food Item > Fuzzy Set Right Linear Fuzzy Set …….. …………

Example: tipper.fml

Example : tipper.fml

Example : tipper.fml food rancid service poor tip cheap

service good tip average

Example : tipper.fml