Artificial Intelligence Lecture No. 32

1. Artificial IntelligenceLecture No. 32 Dr. Asad Ali Safi ​ Assistant Professor, Department of Computer Science,  COMSATS Institute of Information Technology (CIIT) Islamabad, Pakistan.

2. Summary of Previous Lecture • Genetic algorithms • GA Requirements • Theory of Evolution • GA Strengths • GA Weaknesses

3. Today’s Lecture • Fuzzy Logic • Fuzzy Membership Sets • Fuzzy Linguistic Variables • Fuzzy Control

4. 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.

5. "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!

6. Where did it begin? • The concept of Fuzzy Logic (FL) was conceived by LotfiZadeh, 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. 

7. 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.

8. 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}

9. Fuzzy Logic (FL) vs Conventional control methods • 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.

10. Fuzzy Logic (FL) vs Conventional control methods • 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.

11. 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?

12. 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?

13. 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.

14. Create FL membership functions that define the meaning (values) of Input/Output terms used in the rules The features of a membership function

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

16. Membership Functions • How cool is 36 F° ?

17. Inputs: Temperature • Temp: {Freezing, Cool, Warm, Hot}

18. Inputs: Temperature, Cloud Cover • Temp: {Freezing, Cool, Warm, Hot} • Cover: {Sunny, Partly, Overcast}

19. Output: Speed • Speed: {Slow, Fast}

20. 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...

21. Defuzzification: Constructing the Output • Speed is 20% Slow and 70% Fast • Find centroids: Location where membership is 100%

22. Defuzzification: Constructing the Output • Speed is 20% Slow and 70% Fast • Speed = weighted mean = (2*25+...

23. Defuzzification: Constructing the Output • Speed is 20% Slow and 70% Fast • Speed = weighted mean = (2*25+7*75)/(9) = 63.8 mph

24. 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

25. 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

26. Summery of Today’s Lecture • Fuzzy Logic • Fuzzy Membership Sets • Fuzzy Linguistic Variables • Fuzzy Control

27. 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

28. 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

29. 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

30. 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

31. 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

32. Concluding the classes … • Machine learning • Algorithm types • Supervised • Artificial Neural Networks • Perceptrons • Single Layer Perceptron • Multi-Layer Networks Lecture 27 Lecture 28 Lecture 29

33. Concluding the classes … • Unsupervised learning • Self Organizing Map (SOM) • Genetic algorithms • GA Requirements • Theory of Evolution • Fuzzy Logic Lecture 30 Lecture 31 Lecture 32

34. Material used from the following sources • CLIPS Userʼs Guide • Intelligent Systems by Tai-WenYue • 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 LiseGetoor • 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 SarojKausik • Rule-based expert systems by negnevitskypearson 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 YauFenghui • faculty.tnstate.edu/fyao/COMP4400/AI-Chap1and2-4web.ppt‎ • Solving Problems By Searching by Dr MuhamadTounsi PSU • Introduction to Artificial Intelligence by Eyal Amir • www.authorstream.com/.../techi.vaby-1537745-unit-ii-solving-problems.ppt • Expert Systems by SepandarSepehr 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 byHCMC University of Technology • What is an Intelligent Agent ? By Based on Tutorials Monique Calisti ..