Introduction to Artificial Intelligence Artificial Intelligence Section 4 Mr. Sciame.
Artificial Intelligence Lecture No. 32
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Transcript of Artificial Intelligence Lecture No. 32
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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 ..