This document provides an introduction to artificial intelligence techniques. It discusses key concepts such as what intelligence and AI are, why AI is studied, and different fields of AI like expert systems, neural networks, genetic algorithms and more. It also covers topics such as the advantages of using AI, where it should and shouldn't be applied, the differences between soft computing and hard computing, and more.

1. Artificial Intelligence
Lect. (1)
Eng .Mahmoud Hussein

2. Course Content
Introduction to Artificial Intelligence techniques.
Expert Systems.
Fuzzy Logic Controllers.
Neural Network.
Adaptive Neuro-Fuzzy Inference Systems (ANFIS).
Genetic Algorithm.

3. References

4. Outlines
What is Intelligence?
What is AI?
why to study AI?
Fields of AI
Industrial Applications of AI
Advantages of using AI
Where AI should/ shouldn’t be applied?
Soft Computing Vs. Hard Computing

5. What is Intelligence?
The ability to learn or understand things or to deal with
new or difficult situations.
(Webster's dictionary)
The capability of a system to adapt its behavior
(implement decisions) to meet its goals in a range of
environments. It is a property of all purpose-driven
decision makers.
(David Fogel)

6. What is Artificial Intelligence?
 System that think like human … ( General Problem Solver)
 [The automation of] activities that we associate with human thinking,
activities such as decision-making, problem solving, learning... (Bellman,
1978)
 System that think rationally … ( The Laws of Thought Approach)
 The art of creating machines that perform functions that require
intelligence when performed by people (Kurzweil, 1990)
 System that act like human … (Turing Test Approach)
 The study of the computations that make it possible to perceive, reason,
and act (Winston, 1992)
 System that act rationally … (Rational Agent Approach)
 The branch of computer science that is concerned with the automation of
intelligent behavior (Luger and Stubblefield 1993)

7. What is Artificial Intelligence?
Rationality is the ideal concept of intelligence. System is
rational if it is doing the right thing.
This doesn’t mean that human is irrational. But humans
sometimes make mistakes for example;
Not everyone gets A on exams.
Chess game playing, people may make mistakes although
they know the rules very well.

8. What is Artificial Intelligence?
AI is a study about inventing machines/computers that
capable of mimicking human/animal intelligent behavior.
The ultimate objective is to develop a system that can
think and act rationally and like humans.
AI is a “tool” that has been developed to imitate human
intelligence and decision-making functions, providing
basic reasoning and other human characteristics.

9. Why to study AI?
To learn more about ourselves.
Machines with human-level intelligence would have a
huge impact on our everyday lives and the future of
civilization.
AI encompass huge variety of subfields from general
purpose areas such as perception and logical reasoning to
specific tasks such as game playing, theorem proving and
diagnosing diseases.
Building algorithms/hardware that is based on human/animal
intelligence (Copying from biological systems).

10. Why to study AI?
Copying from biological systems

11. Why to study AI?
Copying from biological systems

12. Why to study AI?
Copying from biological systems

13. Fields of AI
 Expert System
 Natural language processing
 Speech (voice) understanding/ recognition
 Robotic & Sensor system
 Text and face recognition
 Intelligent computer aided instruction
 Neural computing
 Machine learning
 Fuzzy logic
 Genetic algorithms
 Intelligent agent

14. Industrial Applications of AI

15. Industrial Applications of AI
Assignment …
Search for available products that uses AI techniques in the
market.

16. Advantages of using AI
Better performance
Longer Life
Reliability
Simpler operation
Cost effective
Higher efficiency
Self-organizing / self-optimization
Simpler design.

17. Where AI should be applied?
Data is overwhelming/abundance
Too many manual operations/procedures
Optimization is possible
Decision making is required
When current techniques are too complicated to be
used/designed
Mathematical models are too complex/impossible
To increase efficiency
To reduce cost
To improve performance and reliability

18. Where AI should not be applied?
Lack of Data
Simpler techniques are available / sufficient
Further optimization is not possible

19. Soft Computing
The concept of Soft Computing (SC) was first initiated by Lotfi
Zadeh in 1981.
Soft Computing definition by Lotfi Zadeh …
“Soft Computing is an emerging approach to computing which
parallel the remarkable ability of the human mind to reason and
learn in an environment of uncertainty and imprecision”
Zadeh, defined the SC as one multidisciplinary system as the
fusion of the fields of Fuzzy logic, Neuro-Computing,
Evolutionary and Genetic Computing and Probabilistic
Computing.

20. Soft Computing
SC is the fusion of methodologies designed to model and
enable solutions to real world problems which are not modeled
or too difficult to be model mathematically.
The aim of SC is to exploit the tolerance for imprecision,
uncertainty, approximate reasoning and partial truth in order to
achieve resemblance to human like decision making.
SC consist of several computing paradigms mainly;
Fuzzy Logic (FL) … (Reasoning and Knowledge Representation)
Neural Network (NN) … (Learning)
Genetic Algorithms (GA) … (Optimization)

21. Soft Computing
SC can be seen as

22. Soft Computing
Hybridization of the three main paradigms (FL, NN and GA)
creates a successful synergic effect that creates a situation
where different entities cooperate advantageously for a final
outcome.

23. Soft Computing (Goals)
The main goal of the SC is to develop intelligent machines to provide
solutions to real world problems which are not modeled or too
difficult to model mathematically.
Its aim is to exploit the tolerance for imprecision, uncertainty,
approximate reasoning and partial truth in order to achieve resemblance
to human like decision making.
Approximation
The model features are similar to the real one, but not the same
Uncertainty
We are not sure that the features of the model as the same as the entity
Imprecision
The model features are not the same as the real ones, but close to them

24. Soft Computing (Importance)
The main benefit of SC that it exploits the tolerance of imprecision,
approximation and uncertainty to achieve robustness and low cost
solution. In fact, the role model for SC is the human mind.
Soft Computing is a partnership among different techniques in which
each of the partners contributes a distinct methodology for addressing
problems in its domain.
In other words … the participating methodologies are complementary
rather than competitive

25. Hard Computing
conventional computing, requires a precisely stated analytical
model and often a lot of computation time
based on binary logic, crisp systems, numerical analysis and
crisp software
has the characteristics of precision

26. Soft Computing Vs. Hard Computing
 Hard computing requires programs to be written; soft computing
can evolve its own programs
 Hard computing uses two-valued logic; soft computing can use
multivalued or fuzzy logic
 Hard computing is deterministic; soft computing can be stochastic
 Hard computing requires exact input data; soft computing can deal
with ambiguous and noisy data
 Hard computing is strictly sequential; soft computing allows
parallel computations
 Hard computing produces precise answers; soft computing can
yield approximate answers