This document provides an introduction to artificial intelligence (AI) and related concepts. It defines key terms like data, information, knowledge and knowledge base. It discusses different views on defining AI, including thinking humanly, thinking rationally, acting humanly, and acting rationally. The document also covers the history of AI, applications of AI, knowledge representation, and the differences between human and artificial intelligence. It provides examples of expert systems like DENDRAL, MYCIN, PUFF and ELIZA to illustrate the concept.

1. Chapter one
AI Introduction
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2.  ObjectiveObjective
 To understand AI and related concept (Knowledge
base and intelligent system)
 Understand the components of AI system
 Get a feel of application areas of AI
 Get a feel of scholars view to define AI
 Briefly discuss the difference between Expert
system and other systems
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3. Data, Information, and Knowledge
• Data: Unorganized and
unprocessed facts; static; a set of
discrete facts about events
• Information: Aggregation of data
that makes decision making easier
• Knowledge is derived from
information in the same way
information is derived from data; it
is a person’s range of information
• What is Data and Information? Are they different
from Knowledge?
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4. What is Knowledge
• Knowledge includes facts about the real world
entities and the relationship between them
– It is an Understanding gained through experience
– familiarity with the way to perform a task
– an accumulation of facts, procedural rules, or heuristics
• Characteristics of Knowledge:
– It is voluminous in nature and requires proper
structuring.
– It may be incomplete and imprecise.
– It may keep on changing (dynamic).
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5. Types (Categorization) of Knowledge
• Shallow (readily recalled) and deep (acquired through
years of experience)
– necessary to make decision/solve problem in complex situations
• Procedural (repetitive, stepwise) versus Episodically
(grouped by episodes or cases)
• Explicit (already represented/documented) and tacit
(embedded in the mind)
– Up to 95% of information is preserved as tacit knowledge
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6. Explicit and Tacit Knowledge
• Explicit (knowing-that)
knowledge:
– knowledge codified and digitized
in books, documents, reports,
memos, etc.
• Tacit (knowing-how)
knowledge:
– knowledge embedded in the
human mind through experience
and jobs
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7. Knowledge base
• Knowledge base is used to store facts and rules.
• In order to solve problems, the computer needs
an internal model of the world.
– This model contains, for example, the description of
relevant objects and the relations between these
objects.
– All information must be stored in such a way that it
is readily accessible.
– Various methods have been used for KR, such as
logic, semantic networks, frames, scripts, etc...
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8. Knowledge base systems (KBSs)
• Deal with treating knowledge and ideas on a computer.
– Emphases to the importance of knowledge.
• Use inference to solve problems on a computer.
– Knowledge-based systems describes programs that reason
over extensive knowledge bases.
• Have the ability to learn ideas so that they can obtain
information from outside to use it appropriately.
– The value of the system lies in its ability to make the workings
of the human mind understandable and executable on a
computer.
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9. Attributes of KBS
•Learn
–The more you use knowledge, the smarter they get and the
smarter you get, too
•Improve with use
–Knowledge is enhanced rather than depleted when used and they
grow up instead of being used up
•Anticipate
–Knowing what you want, KBS recommend what you want next
•Interactive
–There is two-way communication between you and KBS
•Remember
–KBS record and recall past actions to develop a profile
•Customize
–KBS offer unique configuration to your individual specifications in
real time at no additional cost
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10. AI vs. KBS
• Knowledge based system is part of Artificial
Intelligence
• AI also requires extensive knowledge of the
subject at hand.
– AI program should have knowledge base
– Knowledge representation is one of the most
important and most active areas in AI.
– AI programs should be learning in nature and
update its knowledge accordingly.
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11. Intelligence
Intelligence is the capability of observing, learning,
remembering and reasoning.
 Intelligence is a general mental capability that involves the
ability to reason, plan, solve problems, think abstractly,
comprehend ideas and language, and learn.
Intelligence draws on a variety of mental processes,
including memory, learning, perception, decision-
making, thinking, and reasoning.
Memory of Intelligent system is used to store
knowledge base which is the key for success for
artificial intelligent systems.
AI attempts to develop intelligent agents.
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12. Characteristics of Intelligent system
• Use vast amount of knowledge
• Learn from experience and adopt to changing
environment
• Interact with human using language and speech
• Respond in real time
• Tolerate error and ambiguity in communication
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13. Human Intelligence
How do people Reason?
 They create categories
 They use specific rules
– if ‘a’ then ’b’
if ‘b’ then ‘c’
a  b  c
 They use Heuristics - “Rule of thumb”
 They use Past Experience – “CASES”
- Similarities of current and previous case
- Store cases using key attributes
 They Use “Expectations”
 How does our brain work when we solve a problem?
 Do we think it over and suddenly find an answer?
 What do we do when solving a complicated factorization problem, a
puzzle or a mystery?
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14. Artificial Intelligence
• AI is the branch of Computer Science that deals with ways of:
– representing knowledge using symbol rather than numeric
value and with rule-of-thumb and method of processing
information
• AI is the effort to develop computer based system that behave as
human.
–Such system should be able to learn Natural Language.
–Able to do text processing, communicate in natural language
and speech
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15. Views of AI
• AI is found on the premise that:
– workings of human mind can be explained in terms of
computation, and
– computers can do the right thing given correct premises
and reasoning rules.
Views of AI fall into four categories:
Thinking humanly Thinking rationally
Acting humanly Acting rationally
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16. Views of defining AI
• What is AI (Artificial Intelligence)
– Different scholars define AI differently
(A) AI as a system
that think humanly
(C) AI as a system
that Act humanly
(B) AI as a system
that think rationally
(D) AI as a system
that Act rationally
Concerned with
thought processing
and reasoning
Concerned with
behaviors of agents
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17. Views of defining AI
(A) AI as a system
that think humanly
(C) AI as a system
that Act humanly
(B) AI as a system
that think rationally
(D) AI as a system
that Act rationally
measures success of AI in
terms of human being
performance
measures success of AI in terms of
ideal concept of intelligence
(rationality)
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18. Thinking humanly: The Cognitive Modeling
• Reasons like humans do
–Programs that behave like humans
• Requires understanding of the internal activities of the
brain
–see how humans behave in certain situations and see
if you could make computers behave in that same
way.
Example. write a program that plays chess.
–Instead of making the best possible chess-playing
program, you would make one that play chess like
people do.
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19. Acting humanly: The Turing Test
Can machines act like human do? Can machines
behave intelligently?
• Turing Test: Operational test for intelligent behavior
– do experiments on the ability to achieve human-level
performance,
– Acting like humans requires AI programs to interact with
people
• Suggested major components of AI: knowledge,
reasoning, language understanding, learning
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20. Acting humanly: Turing Test
 Turing (1950) on his famous paper "Computing machinery and
intelligence":
 "Can machines think?"  "Can machines behave intelligently?"
 Operational test for intelligent behavior: the Imitation Game
Predicted that by 2000, a machine might have a 30% chance
of fooling a person for 5 minutes
Anticipated all major arguments against AI in following 50
years
Active areas of research to achieve this: Machine learning,
NLP, Computer vision, etc
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21. Thinking Rationally: The Laws of Thought
• A system is rational if it thinks/does the right thing
through correct reasoning.
• Aristotle: provided the correct arguments/ thought
structures that always gave correct conclusions given
correct premises.
– Abebe is a man; all men are mortal; therefore Abebe is
mortal
– These Laws of thought governed the operation of the mind
and initiated the field of Logic.
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22. Acting rationally: The rational agent
• Doing the right thing so as to achieve one’s goal,
given one’s beliefs.
• AI is the study and construction of rational agents
(an agent that perceives and acts)
• Rational action requires the ability to represent
knowledge and reason with it so as to reach good
decision.
– Learning for better understanding of how the
world works
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23. 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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24. History of AI
• Development of knowledge-based systems: the key
to power
– Performance of general-purpose problem solving methods
is weak for many complex domains.
– Use knowledge more suited to make better reasoning in
narrow areas of expertise (like human experts do).
– Early knowledge intensive systems include:
• The Dendral program (1969): solved the problem of inferring
molecular structure (C6H13NO2).
• MYCIN (1976): used for medical diagnosis.
• etc.
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25. History of AI
• Shifts from procedural to declarative programming
paradigm.
– Rather than telling the computer how to compute a
solution, a program consists of a knowledge base of facts
and relationships.
– Rather than running a program to obtain a solution, the
user asks question so that the system searches through
the KB to determine the answer.
• Simulate human mind and learning behavior (Neural
Network, Belief Network, Hidden Markov Models,
etc. )
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26. Applications of AI and KBS
Solving problems that required thinking by humans:
•Playing games (chess, checker, cards, ...)
•Proving theorems (mathematical theorems, laws of
physics, …)
•Classification of text (Politics, Economic, sports, etc,)
•Writing story and poems; solving puzzles
•Giving advice in medicine, law, … (diagnosing diseases,
consultation, …)
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27. How to make computers act like humans?
The following sub-fields are emerged
• Natural Language processing (enable computers
communicate in human language, English, Amharic, ..)
• Knowledge representation (schemes to store information,
both facts and inferences, before and during interrogation)
• Automated reasoning (use stored information to answer questions
and to draw new conclusions)
• Machine learning (adapt to new circumstances and accumulate
knowledge)
• Computer vision (recognize objects based on patterns in the
same way as the human visual system does)
• Robotics (produce mechanical device capable of controlled motion;
which enable computers to see, hear & take actions)
• Is AI equals human intelligence ?
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28. Programming paradigms
• Each programming paradigms consists of two aspects:
–Methods for organizing data/knowledge,
–Methods for controlling the flow of computation
• Traditional paradigms:
Programs = data structure + algorithm
• AI programming paradigms:
Programs = knowledge structure + inference mechanism
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29. Basic Kinds of Systems
• 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:
– Information Systems
– Database Management System
– Information Retrieval System
– Expert System 29

30. Expert system
• An expert system, also known as a knowledge based
system,
• a computer program that contains some of the subject-
specific (domain specific) knowledge of one or more
human experts.
• a system with two basic components:
• Knowledge base, which model the knowledge of an
expert in the area under consideration
• Inference engine
• 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)
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31. Expert system
– Examples:
• Dendral, MYCIN, PUFF, ELIZA, BTDS, etc.
• Dendral expert system:
– The primary aim to aid organic chemists with
identification of unknown organic molecules by
analyzing information from mass spectrometry graphs
and the knowledge of chemistry
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32. Expert system
• MYCIN:
• Written in LISP around 1970s and derived from Dendral
expert system
• was designed to diagnose infectious blood diseases and
recommend antibiotics, with the dosage adjusted for
patient's body weight
• It would query the physician/patient running the
program via a long series of simple yes/no or textual
questions.
• At the end, it provides
• a list of possible cause bacteria ranked from high to low
based on the probability of each diagnosis,
• its confidence in each diagnosis' probability
• the reasoning behind each diagnosis
• It has around ~50 rules
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33. Expert system
• PUFF:
– PUFF can diagnose the presence and severity of lung
disease and produce reports for the patient's file
– Is an Expert System that interprets lung function test
data and has become a working tool in the pulmonary
physiology lab of a large number of hospital
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34. Expert system
• ELIZA :
• ELIZA is a very well-known artificial intelligence
program designed to emulate a Rogerian
psychotherapist.
• The basic elements of Carl Rogers' new way of therapy
was to have a more personal relationship with the
patient, to help the patient reach a state of realization
that they can help themselves
• ELIZA was showcased for a number of years at the
MIT AI Laboratory.
• ELIZA has no reasoning ability, cannot learn
• ELIZA only appears to understand because "she" uses
canned responses based on keywords, as well as string
substitution 34

35. THE DIFFERENCE BETWEEN HUMAN BEINGS
AND ARTIFICIALLY INTELLIGENT SYSTEMS
Human Brain
Natural device
Self-willed and creative
Basic unit – neuron
Storage device –
electrochemical
Low crunching
Advanced detective reasoning
Lower speed
Emotion-driven
Limited volume
Fuzzy logic
Computers
 Non-natural device
 Limited creativity
 Basic unit – RAM
 Storage device is electromechanical
 High crunching
 Elementary detective reasoning
 High speed
 Non-emotional
 Higher volume
 Numeric (binary) logic
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36. What computers can do better
• Numeric computations
• Information storage
• Repetitive operation
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37. How do AI differ from Human Intelligence
• The successful AI systems are neither artificial nor
intelligent
• The successful AI is based on:
– Human expertise
– Knowledge
– Selected reasoning pattern
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38. Problems of AI:
• The following points are taken as drawbacks of
Artificially Intelligent systems:
– AI do not come up with new and novel solutions
– Existing AI systems try to reproduce the expertise
of humans, but do not behave like human experts
– Lack of common-sense
– Reasoning: the human intelligence performs
better in this respect
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