Download - Artificial Intelligence Lecture No. 32

Artificial IntelligenceLecture No. 32

Dr. Asad Ali Safi

Assistant Professor,Department of Computer Science,

COMSATS Institute of Information Technology (CIIT) Islamabad, Pakistan.

Summary of Previous Lecture

• Genetic algorithms• GA Requirements• Theory of Evolution• GA Strengths• GA Weaknesses

Today’s Lecture

• Fuzzy Logic• Fuzzy Membership Sets• Fuzzy Linguistic Variables• Fuzzy Control

What is fuzzy logic?

• Definition of fuzzy

• Fuzzy – “not clear, dissimilar, blurred”

• Definition of fuzzy logic

• A form of knowledge representation suitable for notions that

cannot be defined precisely, but which depend upon their

contexts.

• "Tall Men", "Hot Days", or "Stable Currencies"

• We Will Probably Have a Successful Business Year.

• The Experience of Expert A Shows That B Is Likely to Occur.

However, Expert C Is Convinced This Is Not True.

• "If it is sunny and warm today, I will drive fast"• Linguistic variables:

– Temp: {freezing, cool, warm, hot}– Cloud Cover: {overcast, partly cloudy, sunny}– Speed: {slow, fast}

• Most words and evaluations we use in our daily reasoning are not clearly defined in a mathematical manner. This allows humans to reason on an abstract level!

Where did it begin?• The concept of Fuzzy Logic (FL) was conceived by Lotfi

Zadeh, a professor at the University of California at Berkley, and presented not as a control methodology, but as a way of processing data by allowing partial set membership rather than crisp set membership or non-membership.

• This approach to set theory was not applied to control systems until the 70's due to insufficient small-computer capability prior to that time.

• Professor Zadeh reasoned that people do not require precise, numerical information input, and yet they are capable of highly adaptive control.

Problem solving• FL is a problem-solving control system methodology that

lends itself to implementation in systems ranging from simple, small, embedded micro-controllers to large, networked, multi-channel PC or workstation-based data acquisition and control systems.

• It can be implemented in hardware, software, or a combination of both.

• FL provides a simple way to arrive at a definite conclusion based upon vague, ambiguous, imprecise, noisy, or missing input information.

• FL's approach to control problems mimics how a person would make decisions.

Fuzzy Logic (FL) vs Conventional control methods

• Crisp (Traditional) Variables:• Crisp variables represent precise quantities:

– x = 3.1415296– A {0,1}

• A proposition is either True or False– A B C

• King(Richard) Greedy(Richard) Evil(Richard)• Richard is either greedy or he isn't:

– Greedy(Richard) {0,1}


• FL incorporates a simple, rule-based IF X AND Y THEN Z approach to a solving control problem rather than attempting to model a system mathematically.

• The FL model is empirically-based, relying on an operator's experience rather than their technical understanding of the system. – terms like "IF (process is too cool) AND (process is

getting colder) THEN (add heat to the process)" or – "IF (process is too hot) AND (process is heating rapidly)

THEN (cool the process quickly)" are used.


• These terms are imprecise and yet very descriptive of what must actually happen.

• Consider what you do in the shower if the temperature is too cold: you will make the water comfortable very quickly with little trouble. FL is capable of mimicking this type of behavior but at very high rate.

Fuzzy Sets

• What if Richard is only somewhat greedy? • Fuzzy Sets can represent the degree to which

a quality is possessed.• Fuzzy Sets (Simple Fuzzy Variables) have

values in the range of [0,1]• Greedy(Richard) = 0.7 • Question: How evil is Richard?

Fuzzy Linguistic Variables

• Fuzzy Linguistic Variables are used to represent qualities spanning a particular spectrum

• Temp: {Freezing, Cool, Warm, Hot}• Membership Function• Question: What is the temperature?• Answer: It is warm.• Question: How warm is it?

Membership function• The membership function is a graphical representation of the

magnitude of participation of each input. • It associates a weighting with each of the inputs that are

processed, define functional overlap between inputs, and ultimately determines an output response.

• The rules use the input membership values as weighting factors to determine their influence on the fuzzy output sets of the final output conclusion.

• Once the functions are inferred, scaled, and combined, they are defuzzified into a crisp output which drives the system.

• There are different membership functions associated with each input and output response.

• Create FL membership functions that define the meaning (values) of Input/Output terms used in the rules

The features of a membership function

Membership Functions

• Temp: {Freezing, Cool, Warm, Hot}• Degree of Truth or "Membership"•

50 70 90 1103010

Temp. (F°)

Freezing Cool Warm Hot

0

1

Membership Functions

• How cool is 36 F° ?

50 70 90 1103010

Temp. (F°)


0

1

Inputs: Temperature

• Temp: {Freezing, Cool, Warm, Hot}

50 70 90 1103010

Temp. (F°)


0

1

Inputs: Temperature, Cloud Cover

• Temp: {Freezing, Cool, Warm, Hot}

• Cover: {Sunny, Partly, Overcast}

50 70 90 1103010

Temp. (F°)


0

1

40 60 80 100200

Cloud Cover (%)

OvercastPartly CloudySunny

0

1

Output: Speed

• Speed: {Slow, Fast}

50 75 100250

Speed (mph)

Slow Fast

0

1

Rules

• If it's Sunny and Warm, drive Fast Sunny(Cover)Warm(Temp) Fast(Speed)

• If it's Cloudy and Cool, drive Slow Cloudy(Cover)Cool(Temp) Slow(Speed)

• Driving Speed is the combination of output of these rules...

Defuzzification: Constructing the Output

• Speed is 20% Slow and 70% Fast

• Find centroids: Location where membership is 100%

50 75 100250

Speed (mph)

Slow Fast

0

1



• Speed = weighted mean = (2*25+...

50 75 100250

Speed (mph)

Slow Fast

0

1



• Speed = weighted mean = (2*25+7*75)/(9)= 63.8 mph

50 75 100250

Speed (mph)

Slow Fast

0

1

Notes: Follow-up Points

• Fuzzy Logic Control allows for the smooth interpolation between variable centroids with relatively few rules

• This does not work with crisp (traditional Boolean) logic

• Provides a natural way to model some types of human expertise in a computer program

Notes: Drawbacks to Fuzzy logic

• Requires tuning of membership functions • Fuzzy Logic control may not scale well to large

or complex problems• Deals with imprecision, and vagueness, but

not uncertainty

Summery of Today’s Lecture

• Fuzzy Logic• Fuzzy Membership Sets• Fuzzy Linguistic Variables• Fuzzy Control

Concluding the classes • What is Intelligence ?• What is artificial intelligence?• Intelligent Systems in Your Everyday Life

• How much can be a Machine Intelligent?• Human Intelligence VS Artificial Intelligence• Is AI dangerous?

• Weak and Strong AI• The Turing Test approach• Chinese Room Argument

Lecture 1

Lecture 2

Lecture 3

Concluding the classes… • What is an Intelligent agent?• Agents & Environments• Performance measure, Environment, Actuators, Sensors

• Different types of Environments• IA examples based on Environment• Agent types

• Problem solving by searching• What is Search?• Problem formulation

Lecture 4

Lecture 5

Lecture 6

Concluding the classes … • Uninformed Search• Informed Search • Breadth-first searching• Depth-first search

• Informed (Heuristic) search• Heuristic evaluation function • Greedy Best-First Search• A* Search

• A knowledge-based agent• The Wumpus World

Lecture 7

Lecture 8

Lecture 9

Concluding the classes … • logic• Propositional logic• Pros and cons of propositional logic• First-order logic

• Knowledge• Transfer of knowledge • Types of knowledge• Organizing the Knowledge

• Inheritance in Frames• Semantic network

Lecture 10

Lecture 11

Lecture 12

Concluding the classes … • Rules based Organizing of the Knowledge• Rules can representation • Propositional logic

• Expert System• Forward chaining and backward chaining

• CLIPS

Lecture 13

Lecture 14 15 16

Lecture 17-26

Concluding the classes …

• Machine learning• Algorithm types

• Supervised• Artificial Neural Networks• Perceptrons

• Single Layer Perceptron• Multi-Layer Networks

Lecture 27

Lecture 28

Lecture 29

Concluding the classes …

• Unsupervised learning• Self Organizing Map (SOM)

• Genetic algorithms• GA Requirements• Theory of Evolution

• Fuzzy Logic

Lecture 30

Lecture 31

Lecture 32

Material used from the following sources • CLIPS Userʼs Guide• Intelligent Systems by Tai-Wen Yue • Artificial Intelligence by Reema Tariq• Ihttp://en.wikipedia.org/• ntelligent Agents by Oliver Schulte• Artificial Neural Networks Dr. Duong Tuan Anh• Informed search algorithms by Min-Yen Kan • Heuristic Search by Lise Getoor• Robotics, Artificial Intelligence by Nick Vallidis• MLP by Andy Philippides • http://www.cs.columbia.edu/~kathy/cs4701• genome.tugraz.at/MedicalInformatics2/SOM.pdf • Knowledge-Based Agents by Marie des , Andreas

Schulz and Chuck Dyer• Logical Agents and First Order Logic CSC 8520

Spring 2013. Paula Matuszek• Knowledge Representation Techniques by Saroj

Kausik • Rule-based expert systems by negnevitsky pearson

education 2005 • http://staff.unak.is/not/tony/teaching/ai/lectures/

05aBreadthDepth/breadthDepth.ppt• http://www.seattlerobotics.org/encoder/mar98/

fuz/flindex.html

• Artificial Intelligence: A Modern Approach, Stuart Russell and Peter Norvig, Prentice Hall.

• Artificial Intelligence by Hassan Najadat Jordan UST

• Artificial Intelligence CptS440/540 EECS by Yau Fenghui

• faculty.tnstate.edu/fyao/COMP4400/AI-Chap1and2-4web.ppt

• Solving Problems By Searching by Dr Muhamad Tounsi PSU

• Introduction to Artificial Intelligence by Eyal Amir

• www.authorstream.com/.../techi.vaby-1537745-unit-ii-solving-problems.ppt

• Expert Systems by Sepandar Sepehr McMaster University

• web2.aabu.edu.jo/tool/course_file/lec_notes/901470_exp_system1.ppt

• Informed Search and Exploration by Michael Scherger

• Artificial neural networks by HCMC University of Technology

• What is an Intelligent Agent ? By Based on Tutorials Monique Calisti ..