Introduction Artificial Intelligence
Lecture 1
Dr. Ashraf Osman
ashreca2@gmail.com
Introduction to Artificial Intelligence
 Instructor: ASHRAF OSMAN (PhD)
 Email: ashreca22@gmail.com
 Lecture Hours: Tuesday , 01:00pm-03:00pm,
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Grading:
Include assignments, Project, Mid
term exam and Final exam
Assignments and Projects
 Assignments
and projects will involve
programming using any programming languages
or any simulation tools, that student know.
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Evaluation Method
 The evaluation will consist of three or more components
namely, assignments, projects and a final examination.
 The strategy of these components will be individual or in
group, this depend on the nature of the Assignments,
home works or projects.
 Each of the these components will be given during the
semester and the deadline of the submission is very
important and no any exceptions.
 Late work will be penalized 50% and/or given a zero.
Students need to pass the assignments component and the
final exam component of the course to obtain a passing
grade in this course.
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References
 Michael Negnevitsky, Artificial Intelligence: A Guide to
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Intelligent Systems, 2nd edition.
Toshinori Munakata, Fundamentals of the New Artificial
Intelligence, Neural, Evolutionary, Fuzzy and More,
Second Edition.
Ajith Abraham, “Intelligent Systems: Architectures and
Perspectives”
Amit Konar, “Computational Intelligence Principles,
Techniques and Applications”.2004.
G. Luger “ Artificial Intelligence – Structures and
Strategies for Complex Problem Solving ” AddisionWesley, 2005, Fifth edition
What is Artificial Intelligence (AI)??
 AI is a broad field, and means different things to different
people. It is concerned with getting computers to do tasks
that require human intelligence which require complex and
sophisticated reasoning processes and knowledge.
 If John McCarthy, the father of AI, were to coin a new
phrase for “artificial intelligence” today, he would
probably use “computational intelligence.” (IEEE
Intelligent Systems, 2002)
 HOWEVER, L.A. Zadeh claimed that computational
intelligence is actually Soft Computing techniques (1994)
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 The goal of artificial intelligence (AI) as a science is to
make machines do things that would require intelligence if
done by humans. Therefore, the answer to the question
Can Machines Think? was vitally important to the
discipline.
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What is Intelligence ?
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
Intelligence is the ability to think and understand instead
of doing things by instinct or automatically. (Essential
English Dictionary, Collins, London, 1990).

In order to think, someone or something has to have a
brain, or an organ that enables someone or something to
learn and understand things, to solve problems and to make
decisions. So we can define intelligence as the ability to
learn and understand, to solve problems and to make
decisions.
 The goal of artificial intelligence (AI) as a science is to
make machines do things that would require intelligence if
done by humans. Therefore, the answer to the question
Can Machines Think? was vitally important to the
discipline.
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One way to understand “intelligence” is
by looking at ourselves.
Humans are able to:
 Think
 Understand
 Recognize
 Perceive
 Generalize
 Adapt
 Learn
 Make Decisions
 Solve Daily Problems
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Several Forms of Intelligence
 Capability to learn
 Gathering of information
 Recognizing Patterns
 Capability to classify
 Making decisions
 Reasoning capability
 Predicting/Forecasting
 Capability to survive
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Strong AI vs. Weak AI
 Strong AI
• “An artificial intelligence system can think and have a
mind. “ (John Searle 1986)
• “Machine intelligence with the full range of human
intelligence” (Kurzweil 2005)
• Ai that matches or exceeds human intelligence.
• Intelligence can be reduced to information processing.
• “Science Fiction AI”
 Weak AI
• Intelligence can partially be mapped to computational
processes.
• Intelligence is information processing
• Intelligence can be simulated
Introduction Artificial Intelligence
Lecture 2
Dr. Ashraf Osman
Intelligent Systems (IS)
 Computational systems and methods which simulate
aspects of intelligent behavior. The intention is to learn
from nature and human performance in order to build more
powerful systems.
 The aim is to learn from cognitive science, neuroscience,
biology, engineering, and linguistics for building more
powerful computational system architectures.
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Intelligent Systems
Intelligent Systems Methodologies:
 Expert Systems
 Fuzzy Systems
 Artificial Neural Networks
 Genetic Algorithms
 Case-base reasoning
 Data Mining
 Intelligent Software Agents
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Characteristics of Intelligent Systems
 Possess one or more of these:
•
•
•
•
•
Capability to extract and store and knowledge
Human like reasoning process
Learning from experience or (training)
Deal with imprecise expressions of facts
Finding Solutions through processes similar to natural
evolution
 Recent Trends
 More sophisticated interaction with the user through
• Natural language understanding
• Speech recognition and synthesis
• Image analysis
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 Most current Intelligent Systems are based on:
• Rule based expert systems
• One or more of the methodologies belonging to soft
computing
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A fragment of the Computational Intelligence family tree
Computational Intelligence
Granular Computing
Neuro- Computing
Supervised
Unsupervised
Evolutionary
Computing
Reinforcement
Genetic
Programming
Fuzzy
Sets
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Rough
Sets
Artificial Life
Artificial Immune
Systems
Genetic
Algorithms
Swarm
Intelligence
Probabilistic
Reasoning
A parent node denotes a broad discipline, and a childe node
denotes a subset of its parent.
 Philosophers have been trying for over 2000 years to
understand and resolve two Big Questions of the Universe:
How does a human mind work, and Can non-humans
have minds? These questions are still un-answered.
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 Some people are smarter in some ways than others.
Sometimes we make very intelligent decisions but
sometimes we also make very silly mistakes. Some of us
deal with complex mathematical and engineering problems
but are moronic in philosophy and history. Some people
are good at making money, while others are better at
spending it. As humans, we all have the ability to learn
and understand, to solve problems and to make decisions;
however, our abilities are not equal and lie in different
areas. Therefore, we should expect that if machines can
think, some of them might be smarter than others in some
ways.
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Research in the general area of IS
 Theory and practice of computation for physical systems
 Game playing, photo identification, Robotics and semantic
identification
 Real-time algorithms for measurement, prediction, and
control
 Artificial intelligence and machine learning
 Databases, Internet security, and privacy
 Image processing, character recognition, and control
 Medical areas of applications, MRI, X-Ray image
processing
 Many..many more…
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A.I. In Military
 The U.S is spending as much 100 billion dollars to develop
robots that can aid or replace human soldiers on the front
line. These robots can operate in combat zones with little
supervision.
 Flight simulations and virtual environments help train over
500,000 Soldiers.
http://www.aaai.org/aitopics/pmwiki/pmwiki.php/AITopics/Military
What’s New in A.I.
 Honda has created a helmet-like device that can read
human brain waves and transmit them to humanoid robot.
A person can make the robot perform simple tasks,
including moving Its arm.
 Prototypes of a car with sensors and small motors to
navigate a traffic-laden city street with no driver have been
created.
http://newsfeedresearcher.com/data/articles_t14/honda-robot-brain.html#hdng0
Today’s A.I.
 While military uses have tended to dominate commercial
development of autonomous robots in America, business
opportunities for smart robots are also sizable, according to
experts Japan’s research into intelligent robotics has been
oriented toward helping the nation’s rapidly aging
population perform domestic tasks.
Main events in the history of AI
Period
Key Events
The birth of Artificial
Intelligence
(1943–1956)
McCulloch and Pitts, A Logical Calculus of the Ideas
Immanent in Nervous Activity, 1943
Turing, Computing Machinery and Intelligence, 1950
The Electronic Numerical Integrator and Calculator
project (von Neumann)
Shannon, Programming a Computer for Playing Chess,
1950
The Dartmouth College summer workshop on machine
intelligence, artificial neural nets and automata theory,
1956
Period
Key Events
The rise of artificial
intelligence
(1956–late 1960s)
LISP (McCarthy)
The General Problem Solver (GPR) project (Newell and
Simon)
Newell and Simon, Human Problem Solving, 1972
Minsky, A Framework for Representing Knowledge, 1975
The disillusionment
in artificial
intelligence (late
1960s–early 1970s)
Cook, The Complexity of Theorem Proving Procedures,
1971
Karp, Reducibility Among Combinatorial Problems, 1972
The Lighthill Report, 1971
Period
Key Events
The discovery of
expert systems (early
1970s–mid-1980s)
DENDRAL (Feigenbaum, Buchanan and Lederberg,
Stanford University)
MYCIN (Feigenbaum and Shortliffe, Stanford University)
PROSPECTOR (Stanford Research Institute)
PROLOG - a logic programming language (Colmerauer,
Roussel and Kowalski, France)
EMYCIN (Stanford University)
Waterman, A Guide to Expert Systems, 1986
Period
Key Events
The rebirth of
artificial neural
networks
(1965–onwards)
Hopfield, Neural Networks and Physical Systems with
Emergent Collective Computational Abilities, 1982
Kohonen, Self-Organized Formation of Topologically
Correct Feature Maps, 1982
Rumelhart and McClelland,
Processing, 1986
Parallel
Distributed
The First IEEE International Conference on Neural
Networks, 1987
Haykin, Neural Networks, 1994
Neural Network, MATLAB Application Toolbox (The
MathWork, Inc.)
Period
Key Events
Evolutionary
computation (early
1970s–onwards)
Rechenberg, Evolutionsstrategien - Optimierung
Technischer Systeme Nach Prinzipien der Biologischen
Information, 1973
Holland, Adaptation in Natural and Artificial Systems,
1975.
Koza, Genetic Programming: On the Programming of the
Computers by Means of Natural Selection, 1992.
Schwefel, Evolution and Optimum Seeking, 1995
Fogel, Evolutionary Computation –Towards a New
Philosophy of Machine Intelligence, 1995.
Period
Key Events
Computing with
Words
(late 1980s–onwards)
Zadeh, Fuzzy Sets, 1965
Zadeh, Fuzzy Algorithms, 1969
Mamdani, Application of Fuzzy Logic to Approximate
Reasoning Using Linguistic Synthesis, 1977
Sugeno, Fuzzy Theory, 1983
Japanese “fuzzy” consumer products (dishwashers,
washing machines, air conditioners, television sets,
copiers)
Sendai Subway System (Hitachi, Japan), 1986
The First IEEE International Conference on Fuzzy
Systems, 1992
Kosko, Neural Networks and Fuzzy Systems, 1992
Kosko, Fuzzy Thinking, 1993
Cox, The Fuzzy Systems Handbook, 1994
Zadeh, Computing with Words - A Paradigm Shift, 1996
Fuzzy Logic, MATLAB Application Toolbox (The
MathWork, Inc.)