2. Inexact Reasoning 2 Session 10
Course : T0273 – EXPERT SYSTEMS
Year : 2014
Inexact Reasoning 2 Session 10

3. Learning Outcomes LO 3 : Solve problems by using Expert Systems
After taking this course, students should be expected to understand and explain about inexact reasoning.
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4. Lecture Outline Approximate Reasoning The State of Uncertainty
Some Commercial Applications of Fuzzy Logic
Summary
Exercise
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5. Approximate Reasoning
A theory of uncertainty based on fuzzy logic is primarily concerned with quantifying and reasoning using natural language in which many words have ambiguous meanings.
As we all know, sleeping a little longer is a very inexact term that varies from person to person.
The more complex something is, the more inexact or “fuzzier” it will be.
Fuzzy logic provides a precise approach for dealing with uncertainty which grows out of the complexity of human behavior.
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6. Approximate Reasoning
The term soft computing has come to mean computing that is not based on the classical two-valued logics.
Soft computing includes fuzzy logic, neural networks, and probabilistic reasoning.
The theory has been extended and applied to many fields for a long time, such as automatic camera tracking of an object in space.
Fuzzy logic has also been combined with neural networks in many applications.
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7. Approximate Reasoning
Some applications of fuzzy theory:
Control Algorithms
Medical Diagnosis
Decision Making
Economics
Engineering
Environmental
Literature
Operations Research
Pattern Recognition
Psychology
Reliability
Security
Science
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8. Approximate Reasoning
Translation rules
Fuzzy probability is incorporated into the fuzzy logic called FL. One main component of FL is a group of translation rules.
Type I: modification rules
Type II: composition rules
conditional composition
conjunctive composition
disjunctive composition
conditional and conjunctive composition
Type III: quantification rules
Type IV: qualification rules
truth qualification
probability qualification
possibility qualification
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9. The State of Uncertainty
There are two mountains that stand out from all the trees and forests:
Mountain of Logic
Mountain of Uncertainty
An expert system must come up with valid conclusions given that:
The rules were written correctly.
The facts on which the inference engine generates valid conclusions are true facts.
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10. The State of Uncertainty
Mountain of Uncertainty
The best we can do on this mountain is model it on the expertise of our expert, or try to include more than one approach to uncertainty and let the different techniques fight it out.
Today fuzzy logic and Bayesian theory are most often used for uncertainty.
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11. Some Commercial Applications of Fuzzy Logic
Many commercial applications of fuzzy logic are in everything from cameras to washing machines:
Hydroelectric-powerplants
Robots
Camera aiming
Assessment of stock exchange activities
Air-conditioning systems
Car-engines
Automobiles
Industrial control applications
Production of semiconductors
Bus time-tables
etc.
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12. exercise Create a program for tipper (based on example in MATLAB)
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14. Rule editor
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15. Summary
Fuzzy logic provides a precise approach for dealing with uncertainty which grows out of the complexity of human behavior.
Soft computing includes fuzzy logic, neural networks, and probabilistic reasoning.
Fuzzy probability is incorporated into the fuzzy logic called FL.
There are two mountains that stand out from all the trees and forests: Mountain of Logic and Mountain of Uncertainty
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16. Exercise Answer the questions below:
Define at least six values for the linguistic variable Water Temperature.
Draw the appropriate functions for the fuzzy set values on one graph.
Given numeric truth values,
x(A) = .2/ /.5 + 1/.9
x(B) = .1/ /.5 + 1/.9
calculate the fuzzy logic truth of the following:
a) NOT A d) A  B
b) A AND B e) B  A
c) A OR B
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17. References
Joseph Giarratano, Gary Riley Expert Systems: Principles and Programming Chapter 5. Thomson Course Technology. Australia. ISBN:
Peter Jackson Introduction to Expert Systems. Addison-Wesley. Harlow, England. ISBN:
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