2. Introduction to Artificial Intelligence and Soft Computing Session 1
Course : T0423-Current Popular IT III
Year : 2013
Introduction to Artificial Intelligence and Soft Computing Session 1

3. Lecture Outline Introduction to Artificial Intelligence
Introduction to Hard and Soft Computing
The utility of Fuzzy systems
Introduction to Fuzzy Logic
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4. Lecturer Dr. Widodo Budiharto D2637
HP :
Quiz 3x
TM 3x
Final Project presentation(Group) at session 13
Reference book:
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5. Intro What is Fuzzy Logic
Demo Video
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6. Artificial Intelligence
Definition :
a branch of computer science dealing with the
simulation of intelligent behavior in computers
(2) the capability of a machine to imitate intelligent human
behavior (merriam-webster.com)
The branch of computer science concerned with making computers behave like humans. (John McCarty)
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7. Artificial Intelligence Domain Area
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8. Soft Computing
Soft computing is an association of computing methodologies that includes fuzzy logic, neuro-computing, evolutionary computing, and probabilistic computing. (P. Bonissone, 2000).
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9. Soft Computing
Hard Computing (conventional) requires a precisely stated analytical model and often a lot of computation time. Many analytical models are valid for ideal cases. Real world problems exist in a non-ideal environment.
Soft computing was introduced by Prof. Lotfi Zadeh in It is a collection of some biologically-inspired methodologies such as Fuzzy Logic, Neural Network, Genetic Algorithm, and combined forms such as GA-FL, GA-NN, NN-FL.
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10. Hard Vs Soft Computing
Hard computing based on binary logic, crisp systems, numerical analysis and crisp software (exp. PID Controller) but soft computing based on fuzzy logic, neural nets and probabilistic reasoning.
Soft computing is tolerant of imprecision, uncertainty, partial truth, and approximation.
Hard computing requires exact input data; soft computing can deal with ambiguous and noisy dat
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11. Demo Hard computing
Demo Video
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12. A review of Soft Computing components
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13. Methods of Soft Computing
Fuzzy Logic
Neural Network Theory
Probabilistic Reasoning
Evolutionary Computing
Genetic Algorithm
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14. Evolutionary computing
Is a subfield of artificial intelligence (computational intelligence) that involves continuous optimization and combinatorial optimization problems.
Evolutionary computing uses iterative progress, such as growth or development in a population. This population is then selected in a guided random search using parallel processing to achieve the desired end.
Such processes are often inspired by biological mechanisms of evolution.
Evolutionary programming was introduced by Lawrence J. Fogel in the US, while John Henry Holland called his method a genetic algorithm
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15. The Utility of Fuzzy Systems
Several sources have shown and proven that fuzzy systems are universal approximators. Hence, fuzzy systems are very useful in two general contexts:
in situations involving highly complex systems whose behaviors are not well understood, and
(2) In situations where an approximate, but fast, solution is
warranted.
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16. Fuzzy Logic
Fuzzy logic is a form of many-valued logic or probabilistic logic; it deals with reasoning that is approximate rather than fixed and exact. In contrast with traditional logic they can have varying values, where binary sets have two-valued logic, true or false, fuzzy logic variables may have a truth value that ranges in degree between 0 and 1.
Fuzzy logic has been extended to handle the concept of partial truth, where the truth value may range between completely true and completely false
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17. Traditional logic Slow Fast Speed = 1 bool speed; get the speed
if ( speed == 0) {
// speed is slow }
else {
// speed is fast
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18. Fuzzy Logic representation
Slowest
For every problem must represent in terms of fuzzy sets.
What are fuzzy sets?
[ 0.0 – 0.25 ]
Slow
[ 0.25 – 0.50 ]
Fast
[ 0.50 – 0.75 ]
Fastest
[ 0.75 – 1.00 ]
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19. Why using Fuzzy Logic
Fuzzy logic allows for approximate values and inferences as well as incomplete or ambiguous data (fuzzy data) as opposed to only relying on crisp data (binary yes/no choices).
Fuzzy Logic provides a more efficient and resourceful way to solve Control Systems.
On 2000, Fuzzy Logic Becomes a Standard Technology and Is Also Applied in Data and Sensor Signal Analysis.
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20. Application of Fuzzy Logic
Sendai Subway system in Sendai, Japan. This control of the Nanboku line, developed by Hitachi, used a fuzzy controller to run the train all day long.
The most tangible applications of fuzzy logic control have appeared commercial appliances. Specifically, but not limited to heating ventillation and air conditioning (HVAC) systems.
Fuzzy logic also finds applications in many other systems. For example, the MASSIVE 3D animation system for generating crowds uses fuzzy logic for artificial intelligence.
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21. History (1965 by Zadeh)
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22. Fuzzy Sets and Membership
Classical sets contain objects that satisfy precise properties of membership; fuzzy sets contain objects that satisfy imprecise properties of membership, i.e., membership of an object in a fuzzy set can be approximate.
For example, the set of heights from 5 to 7 feet is precise (crisp); the set of heights in the region around 6 feet is imprecise, or fuzzy.
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23. Fuzzy Sets and Membership (2)
For crisp sets (Himpunan klasik) an element x in the universe X is either a member of some crisp set A or not. This binary issue of membership can be represented mathematically with the indicator function,
Nilai keanggotaan
Fungsi keanggotaan
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24. Fuzzy Sets Theory Classical Set vs Fuzzy set Membership value1 1
175
Height(cm)
175
Height(cm)
Universe of discourse

25. Fuzzy Sets and Membership (4)
The membership function embodies the mathematical representation of membership in a set, and the notation used throughout this text for a fuzzy set is a set symbol with a tilde underscore, where the functional mapping is given by :
And the symbol is the degree of membership of element x in fuzzy set A.
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26. example
the meanings of the expressions cold, warm, and hot are represented by functions mapping a temperature scale.
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27. Example
The problem is to estimate the level of risk involved in a software engineering project. For the sake of simplicity we will arrive at our conclusion based on two inputs: project funding and project staffing.
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28. Project Funding
Suppose our our inputs are project_funding = 35% and project_staffing = 60%.
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29. Project Staffing
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30. The rules
If project_funding is adequate or project_staffing is small then risk is low.
If project_funding is marginal and project_staffing is large then risk is normal.
If project_funding is inadequate then risk is high.
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31. Rule Evaluation results
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32. Rule Evaluation results
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33. Rule Evaluation results
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34. calculation
We perform a union on all of the scaled functions to obtain the final result
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35. Result
The defuzzification can be performed in several different ways. The most popular method is the centroid method.
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36. Result
We chose the centroid method to find the final non-fuzzy risk value associated with our project. This is shown below.
The result is that this project has 67.4% risk associated with it given the definitions above.
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37. Fuzzy Logic Type 2
To handle uncertainty better than Fuzzy Logic

38. Homework Develop simple Fuzzy logic program using example in C:
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39. References Fuzzy Logic with Engineering Applications, chapter 1.
Artificial Inteligence : A Modern Approach, chapter 1.
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40. Projects paper (Group)
Create a paper and Discuss at session 10 and 13 (submission)
Example topics :
Games using fuzzy logic
Decision system using fuzzy
Interval type 2 FLC
Obstacle avoidance for mobile robot using fuzzy
Example paper :
Octavia George, Maria Gorethi,  Syerra Riswandi and Widodo Budiharto, The Development of an Expert Mood Identifier System using Fuzzy Logic on BlackBerry Platform, Journal of Computer Science, vol 9(6),  pp ,  USA, Indexed by SCOPUS
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