Artificial Intelligences -CHAPTER 1

1. Wallaga University
Artificial Intelligence
Chapter one: Introduction to A I

2. 1.Introduction
 The overall research goal of artificial intelligence is to create
technology that allows computers and machines to function in
an intelligent manner.
 Artificial Intelligence exists when a machine can have human-
based skills such as learning, reasoning, and solving problems
 The field of artificial intelligence, or AI, attempts to
understand intelligent entities.
 Thus, one reason to study it is to learn more about ourselves.
 But unlike philosophy and psychology, which are also
concerned with intelligence, AI strives to build intelligent
entities as well as understand them

3. cont..
With Artificial Intelligence you do not need to preprogram a machine to do
some work, despite that you can create a machine with programmed algorithms
which can work with own intelligence, and that is the awesomeness of AI.
Why Artificial Intelligence?
• Before Learning about Artificial Intelligence, we should know that what is the
importance of AI and why should we learn it.
• Following are some main reasons to learn about AI:
 With the help of AI,
• you can create such software or devices which can solve real-world problems
very easily and with accuracy such as health issues, marketing, traffic issues,
etc.

4. cont..
• you can create your personal virtual Assistant, such as Google
Assistant, Siri, etc.
• you can build such Robots which can work in an environment
where survival of humans can be at risk.
• AI opens a path for other new technologies, new devices, and new
Opportunities..

5. 1.1 Definition of AI
The definition of AI iterates around thought, reasoning and behavior
"The exciting new effort to make computers think . . . machines with
minds, in the full and literal sense" (Haugeland, 1985)
[The automation of] activities that we associate with human
thinking, activities such as decision-making, problem solving,
learning ..."(Bellman, 1978)
AI is one of the fascinating and universal fields of Computer
science which has a great scope in future. AI holds a tendency to
cause a machine to work as a human.

6. Definition of AI….
Artificial Intelligence is composed of two words Artificial and Intelligence.
Artificial defines "man-made," and intelligence defines "thinking power", or “the
ability to learn and solve problems” hence Artificial Intelligence means "a man-
made thinking power."
 Artificial Intelligence (AI) as the branch of computer science by which we can
create intelligent machines which can behave like a human, think like humans,
and able to make decisions.
 Intelligence is the ability to acquire and apply knowledge.
 Knowledge is the information acquired through experience
 Experience is the knowledge gained through exposure (training). Summing the
terms up, we get artificial intelligence as the “copy of something natural(i.e.,
human beings) .”

7. Cont’d…..
"The study of mental faculties through the use of computational
models" (Charniak and McDermott, 1985)
 "The study of the computations that make it possible to perceive,
reason, and act" (Winston, 1992)
"A field of study that seeks to explain and emulate intelligent
behavior in terms of computational processes" (Schalkoff, 1 990)
 "The branch of computer science that is concerned with the
automation of intelligent behavior" (Luger and Stubblefield, 1993)
In summary, the definitions are organized into four categories:
Systems that think like humans.
Systems that act like humans.
Systems that think rationally.
Systems that act rationally.

8. Acting humanly: The Turing Test approach
 AI as systems that act humanly
 “The art of creating machines that perform functions that require
intelligence when performed by people.” (Kurzweil definition, 1990)
 “The study of how to make computers do things at which, at the moment,
people are better.” (Rich and Knight definition, 1991)
 The Turing Test, proposed by Alan Turing (1950), was designed to provide
a satisfactory operational definition of intelligence.
 Turing defined intelligent behavior as the ability to achieve human-level
performance in all cognitive tasks, sufficient to fool an interrogator.
 Roughly speaking, the test he proposed is that the computer should be
interrogated by a human via a teletype, and passes the test if the
interrogator cannot tell if there is a computer or a human at the other end
 For a computer to pass the test would need to possess the following
capabilities
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9. Cont’d
• Natural language processing
– to enable it to communicate successfully in English (or some other
human language)
• Knowledge representation
– to store information provided before or during the interrogation;
• Automated reasoning
– to use the stored information to answer questions and to draw new
conclusions;
• Machine learning
– to adapt to new circumstances and to detect and extrapolate patterns
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10. Thinking humanly: The cognitive modeling approach
• If we are going to say that a given program thinks like a human, we
must have some way of determining how humans think.
• We need to get inside the actual workings of human minds.
• Once we have a sufficiently precise theory of the mind,
– it becomes possible to express the theory as a computer
program.
• If the program's input/output and timing behavior matches human
behavior,
– that is evidence that some of the program's mechanisms may
also be operating in humans
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11. Thinking rationally: The laws of thought approach
• Aristotle was one of the first to attempt to codify "right thinking,"
that is, irrefutable reasoning processes
– correct conclusions given correct premises
• For example,
Socrates is a man;
all men are mortal;
therefore Socrates is mortal."
• These laws of thought were supposed to govern the operation of the
mind, and initiated the field of logic
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12. Acting rationally: The rational agent approach
Acting rationally means acting so as to achieve one's goals,
given one's beliefs.
An agent is just something that perceives and acts
 In this approach, AI is viewed as the study and
construction of rational agents
In the "laws of thought" approach to AI, the whole
emphasis was on correct inferences.
 Making correct inferences is sometimes part of being a
rational agent, because one way to act rationally is to reason
logically to the conclusion that a given action will achieve
one's goals, and then to act on that conclusion.

13. What is intelligence composed of?
Intelligence is composed of:
 Reasoning
 Learning
 Problem Solving
 Perception
 Linguistic Intelligence
Need for Artificial Intelligence
1. To create expert systems. That exhibit intelligent behavior with the
capability to learn, demonstrate, explain and advice its users.
2. To find solutions to complex problems. means helping machines
find solutions to complex problems like humans do and applying them
as algorithms in a computer-friendly manner.

14. Goals of Artificial Intelligence
Following are the main goals of Artificial Intelligence:
1. Replicate human intelligence
2. Solve Knowledge-intensive tasks
3. An intelligent connection of perception and action
4. Building machines w/c can perform tasks that requires human
intelligence.
5. Creating some system. w/c can learn new things by itself, explain
and can advice to its user.

15. What Comprises to Artificial Intelligence?
AI is so vast and requires lots of other factors which can contribute to
it. To create the AI first we should know that how intelligence is
composed, so the Intelligence is an intangible part of our brain which
is a combination of Reasoning, learning, problem-solving
perception, language understanding, etc.
 To achieve the above factors for a machine or software Artificial
Intelligence requires the following discipline:
 Mathematics, Biology, Psychology, Sociology, Computer Science,
Neurons Study, Statistic

16. Foundation of AI
• Although AI itself is a young field, it has inherited many ideas, viewpoints, and
techniques from other disciplines
• Philosophy
– materialism, which holds that all the world (including the brain and mind)
operate according to physical law
– Other argue that the mind has a physical basis, but denies that it can be
explained by a reduction to ordinary physical processes
– philosophy had thus established a tradition in which the mind was conceived
of as a physical device operating principally by reasoning with the
knowledge that it contained.
• Mathematics
– AI needs a formal science i.e. a level of mathematical formalization in three
main areas: computation, logic, ALGORITHM and probability
– Probability:
• invaluable part of all the quantitative sciences, helping to deal with
uncertain measurements and incomplete theories
• degree of belief rather than an objective ratio of out come
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17. Cont’d
• Psychology
– we have the tools with which to investigate the human mind
– specified the three key steps of a knowledge-based agent:
(1) the stimulus must be translated into an internal representation,
(2) the representation is manipulated by cognitive processes to derive new
internal representations, and
(3) these are in turn retranslated back into action.
– clearly explained this a good design for an agent
• Linguistic
– we have theories of the structure and meaning of language
– AI requires an understanding of the subject matter and context, and
understanding of the structure of sentences.
• Computer science
– Finally, from computer science, we have the tools with which to make AI a
reality
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18. Advantages and Disadvantage of AI
Advantage of AI
High Accuracy with fewer errors:
 High-Speed:
 High reliability:
Useful for risky areas:
Digital Assistant:
 Useful as a public utility.
Disadvantages of AI
High Cost:
Can't think
 No feelings and emotions:
Increase dependence on machines
No Original Creativity

19. Applications of AI
Solving problems that required thinking by humans:
• Playing games (chess, checker, cards, ...)
• Proving theorems (mathematical theorems, laws of
physics, …)
• Classification of text (Politics, Economic, Social,
Sports, etc,)
• Information filtering and summarization of text
• Giving advice in Medical diagnosis,

20. Strong AI vs. Weak AI
• Strong AI argues that it is possible that one day a computer will be
invented which can be called a mind in its fullest sense.
– Strong AI aims to create an agent that can replicate humans
intelligence completely; i.e., it can think, reason, imagine, etc., &
do all the things that we currently associate with the human brain.
• Weak AI, on the other hand, argues that computers can only appear to
think & are not actually conscious in the same way as human brains
are.
– The weak AI position holds that AI should try to develop systems
which have facets of intelligence, but the objective is not to build
a completely sentient entity.
– Weak AI researchers see their contribution as things like expert
systems used for medical diagnosis, which use "intelligent"
models, but they do not help create a conscious agent

21. Some Application areas of AI
 AI system is used in.
– Game playing
• Chess game which play at master level use a brute force
computation– to look at hundreds of thousands of positions
– Speech recognition
• It is possible to instruct some computers using speech
(keyboard and mouse)
– Understanding natural language
– Expert systems
– Others
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22. Cont.
• System is a set of components that interact to each other in a
logical way to achieve specific goals.
• There are different types of system based on the services, the
user type, and the method of operations
• Some of the systems includes:
– Database Management System
– Information Retrieval System
– Expert System
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23. Cont.
• Data Base Management System
– DBMS is a software designed for the purpose of managing a
database
– A database is a structured collection of records or data that is
stored in a computer so that a program can consult it to answer
queries.
• Information Retrieval System
– It is the science of
• searching for information in documents,
– It is used to reduce information overloading
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24. Expert system
• An expert system is a computer program that contains some of the subject-
specific (domain specific) knowledge of one or more human experts.
• An expert system is an interactive computer-based decision tool that uses
both facts and heuristics to solve problems, based on knowledge acquired
from an expert
• Are programs that achieve expert-level competence in solving problems in
task areas by using knowledge of a specific tasks
• Every expert system consists of two Building Blocks parts:
– the knowledge base; and
– Inference engine
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25. Knowledge Base
• Knowledge base contains all necessary knowledge about
the domain that is required to handle problems.
• The knowledge can be acquired from experts, documents,
books and/or other sources.
• It is formalized and organized so as to be used by the
computer by using Rule , Cases…..
• Rules are one of the most popular knowledge representations.
• The basic form of a rule is
if <conditions> then <conclusion>
– where <conditions> represent premises and
– <conclusion> represents associated action for the premises.
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26. Inference engine
• After the system gets the required knowledge, it needs
to be instructed how to use the knowledge in solving
problems.
• Inference engine represents the reasoning technique
that manipulates, uses and controls the knowledge to
solve problems.
– Data-driven also called forward chaining
– Goal-driven also called backward chaining
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27. Forward Chaining
• In this process, it receives a problem description as a set of
conditions and tries to derive conclusions as a solution.
• With data-driven control, rules are applied whenever their left-
hand-side conditions are satisfied.
• To use this strategy, one must begin by entering information about
the current problem as facts to the system
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28. Backward Chaining
• This strategy focuses its efforts by only considering rules that are
applicable to some particular goal.
• It is similar with forward chaining in most process, the big
difference is it receives the problem description as set of
conclusions, instead of conditions, and tries to find the premises or
causes of the conclusions.
• Application of Expert system
– Diagnosis and Troubleshooting of Devices and Systems of All
Kinds
– Financial Decision Making
– Medical diagnosis etc…..
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29. Cont.,
• BENEFITS TO END USERS
– A speed-up of human professional or semi-professional work
– Within companies, major internal cost savings
– Improved quality of decision making
– Preservation of scarce expertise
– When it is used by non expert user, it can serve as an expert that
guide the user to make an expert decision. (doctors, engineers,
lawyers, etc)
• Examples:
– Dendral, MYCIN, PUFF, ELIZA, BTDS, etc 29

30. History of AI
Formally initiated in 1956 and the name AI was coined by John
McCarthy.
The advent of general purpose computers provided a vehicle for
creating artificially intelligent entities.
Used for solving general-purpose problems
Which one is preferred?
General purpose problem solving systems
Domain specific systems
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31. Summary on history of AI
• 1943 McCulloch & Pitts: Boolean circuit model of
brain
• 1950 Turing's "Computing Machinery and
Intelligence"
• 1956 Dartmouth meeting: "Artificial Intelligence"
adopted
• 1952—69 Look, Ma, no hands!
• 1950s Early AI programs, including Samuel's checkers
program, Newell & Simon's Logic Theorist,
Gelernter's Geometry Engine
• 1965 Robinson's complete algorithm for logical
reasoning
• 1966—73 AI discovers computational complexity
Neural network research almost disappears
• 1969—79 Early development of knowledge-based
systems
• 1980-- AI becomes an industry
• 1986-- Neural networks return to popularity
• 1987-- AI becomes a science
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32. End of Chapter one