artificial intelligence lecture no. 32
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Artificial Intelligence Lecture 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 - PowerPoint PPT PresentationTRANSCRIPT
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Artificial IntelligenceLecture No. 32
Dr. Asad Ali Safi
Assistant Professor,Department of Computer Science,
COMSATS Institute of Information Technology (CIIT) Islamabad, Pakistan.
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Summary of Previous Lecture
• Genetic algorithms• GA Requirements• Theory of Evolution• GA Strengths• GA Weaknesses
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Today’s Lecture
• Fuzzy Logic• Fuzzy Membership Sets• Fuzzy Linguistic Variables• Fuzzy Control
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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.
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• "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!
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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.
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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.
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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}
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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.
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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.
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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?
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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?
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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.
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• Create FL membership functions that define the meaning (values) of Input/Output terms used in the rules
The features of a membership function
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Membership Functions
• Temp: {Freezing, Cool, Warm, Hot}• Degree of Truth or "Membership"•
50 70 90 1103010
Temp. (F°)
Freezing Cool Warm Hot
0
1
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Membership Functions
• How cool is 36 F° ?
50 70 90 1103010
Temp. (F°)
Freezing Cool Warm Hot
0
1
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Inputs: Temperature
• Temp: {Freezing, Cool, Warm, Hot}
50 70 90 1103010
Temp. (F°)
Freezing Cool Warm Hot
0
1
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Inputs: Temperature, Cloud Cover
• Temp: {Freezing, Cool, Warm, Hot}
• Cover: {Sunny, Partly, Overcast}
50 70 90 1103010
Temp. (F°)
Freezing Cool Warm Hot
0
1
40 60 80 100200
Cloud Cover (%)
OvercastPartly CloudySunny
0
1
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Output: Speed
• Speed: {Slow, Fast}
50 75 100250
Speed (mph)
Slow Fast
0
1
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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...
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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
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Defuzzification: Constructing the Output
• Speed is 20% Slow and 70% Fast
• Speed = weighted mean = (2*25+...
50 75 100250
Speed (mph)
Slow Fast
0
1
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Defuzzification: Constructing the Output
• Speed is 20% Slow and 70% Fast
• Speed = weighted mean = (2*25+7*75)/(9)= 63.8 mph
50 75 100250
Speed (mph)
Slow Fast
0
1
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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
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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
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Summery of Today’s Lecture
• Fuzzy Logic• Fuzzy Membership Sets• Fuzzy Linguistic Variables• Fuzzy Control
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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
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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
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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
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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
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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
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Concluding the classes …
• Machine learning• Algorithm types
• Supervised• Artificial Neural Networks• Perceptrons
• Single Layer Perceptron• Multi-Layer Networks
Lecture 27
Lecture 28
Lecture 29
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Concluding the classes …
• Unsupervised learning• Self Organizing Map (SOM)
• Genetic algorithms• GA Requirements• Theory of Evolution
• Fuzzy Logic
Lecture 30
Lecture 31
Lecture 32
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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 ..