AI_Unit_IARTIFICIAL INTELLIGENCE UNIT 1 OSMANIA UNIVERSITY

Artificial Intelligence
Prepared By
S.Priyanka,
Assistant Professor,
Dept. of CSE,
GLEC

SCHEME OF INSTRUCTION & EXAMINATION
• B.E (INFORMATION TECHNOLOGY)
• V Semester
• PC503IT
• Instruction : 3 +1 periods per week
• Duration of SEE : 3 hours
• CIE : 30 marks
• SEE : 70 marks
• Credits 3
Faculty of Engineering, OU B.E.(I.T.) w.e.f. 2023 - 2024

Syllabus
• UNIT-I:
Introduction- What is intelligence? Intelligent Systems, Foundations
of artificial intelligence (AI), History of AI, Subareas of AI, Applications.
Structure of Agents.
Problem Solving - State-Space Search and state space representation.
• UNIT-II:
Search strategies. - Uninformed Search strategies-BFS,DFS, Iterative
deepening DFS, Informed Search Strategies- Best first search, A*
algorithm, heuristic functions, Iterative deepening A*
• UNIT-III:
Probabilistic Reasoning: Probability, conditional probability, Bayes Rule, Bayesian
Networks- representation, construction and inference, temporal model, hidden
Markov model.

Syllabus contd…
• UNIT-IV:
Expert System and Applications: Introduction, Phases in Building
Expert Systems, Expert System Architecture, Applications.
Markov Decision process: MDP formulation, utility theory, utility
functions, value iteration, policy iteration and partially observable
MDPs.
• UNIT-V:
Reinforcement Learning: Passive reinforcement learning, direct
utility estimation, adaptive dynamic programming, temporal
difference learning, active reinforcement learning- Q learning.

Suggested Readings:
• 1. Stuart Russell and Peter Norvig. Artificial Intelligence – A
Modern Approach, Third edition, Pearson Education Press,.
• 2. Kevin Knight, Elaine Rich, B. Nair, Artificial Intelligence,
McGraw Hill, 3 rd ed, 2009.
• 3. Nils J. Nilsson, The Quest for Artificial Intelligence, Cambridge
University Press, 2009
• 4. David Poole and Alan Mackworth, ―Artificial Intelligence:
Foundations for Computational Agents , Cambridge University
‖
Press 2010.
• 5. Saroj Kaushik, Artificial Intelligence, Cengage Learning, 2011
• 6. K.R.Chowdhary, Fundamentals of AI, Springer, 2020

AI_Unit_IARTIFICIAL INTELLIGENCE UNIT 1 OSMANIA UNIVERSITY

UNIT-I
•Introduction-
What is intelligence?
Intelligent Systems,
Foundations of artificial intelligence (AI),
History of AI,
Subareas of AI,
Applications,
Structure of Agents.
•Problem Solving –
State-Space Search and state space representation.

Introduction
• A.I. – Artificial Intelligence
• Artificial Intelligence is composed of two words
Artificial and Intelligence,
where Artificial defines "man-made," and
intelligence defines "thinking power",
hence AI means "a man-made thinking power.“
• We call ourselves Homo sapiens
The meaning of HOMO SAPIENS is humankind.
• (Latin: “wise man”) the species to which all modern human
beings belong.
• —man the wise—because
• our intelligence is so important to us.

What is Intelligence?
• Intelligence is the ability to learn and solve problems, to deal
with new situations.
• Intelligence is the ability to acquire, understand and apply the
knowledge to achieve goals in the world.
• Intelligence is the ability to take right decision.
• For thousands of years, we have tried to understand how we think
and act; that is, how our brain, a mere handful of matter, can
perceive(see,observe,notice,recognize,identify,distinguish),unders
tand, predict, and manipulate a world far larger and more
complicated than itself.

What is Intelligence Composed of?
• Intelligence is a property of mind that encompasses many related mental abilities.
• It is composed of the following or Some of the capabilities are as follows:
• Reasoning: Reason and draw meaningful conclusions
• Inferencing: conclusion reached on the basis of evidence
• Planning: Plan sequence of actions to complete a goal
• Learning: Learn new ideas from environment and new circumstances, learn new concepts and tasks that
require high levels of intelligence
• Problem Solving.
• Thinking abstractly.
• Linguistic Intelligence(language understanding)
• Comprehend ideas and help computers to communicate in Natural Language(NL)
• Perception(Awareness, Observation)
• Store knowledge
• Offer advice based on rules and situations
• Note: AI PROGRAMS MUST HAVE CAPABIILITY AND CHARACTERISTICS OF INTELLIGENCE

It is composed of-
• Reasoning: set of processes that enables us to provide basis for
judgement, making decisions and predictions.
• Learning: ability to gain knowledge or skill by studying, practising or
experiencing something.
• Inferencing: conclusion reached on basis of evidence.
• Problem solving: process in which one perceives and tries to arrive at a
desired solution from a present situation by taking some path.
• Perception: Process of acquiring, interpreting, selecting and organising
sensory information.
• Linguistic Intelligence: ability to use, comperhend, speak, and write
the verbal and written language.It is important in interpersonal
communication.

Intelligent Systems - A.I
• AI is the study of making computers do things intelligently.
• AI is a branch of computer science which is concerned with the study and
creation of computer systems that exhibit some form of
intelligence Or the characteristics which we associate
with intelligence in human behavior
• One of the founders of AI, John McCarthy(in 1956) stated that
• “AI is the science and engineering of making intelligent machines,
especially intelligent computer programs”.
• Artificial Intelligence is concerned with the design of intelligence in an
artificial device(intelligence exhibited by artificial entity).
• AI refers to the simulation of human intelligence in machines that are
programmed to think and act like humans (Putting human intelligence into
machines).

INTELLIGENT SYSTEMS
• The field of artificial intelligence, AI, attempts not just to
understand but also to build intelligent entities –
• Intelligent systems are technologically advanced machines that perceive
and respond to the world around them. Intelligent systems can take many
forms, from automated vacuums such as the Roomba to facial
recognition programs to Amazon's personalized shopping suggestions.
• An intelligent system is an advanced computer system that can gather,
analyze and respond to the data it collects from its surrounding
environment.

Contd..
• An intelligent system is a system that can imitate, automate some intelligent
behaviours of human being.
• Ex: Expert systems, intelligent agents and knowledge-based systems
• Intelligent systems perform search and optimization along with learning
capabilities. So they are technologically advanced machines that perceive and
respond to the world around them.
• The field of intelligent systems also focuses on how these systems interact
with human users in changing and dynamic physical and social environments.

Definition of AI
• No unique
definition, as
different people
think AI
differently
• Various authors
have defined AI
differently:

ELIZA
• First program, so-called intelligent system, to converse in English.
• Written by Joseph Weizenbaum during 1964 to 1966
• It passed turing test.
• A system is said to have passed Turing test if a human questioner is unable to
determine from repeated questions of any kind, whether he or she is talking to
another person or to a machine system
• ELIZA was one of the first chatterbots.
• https://web.njit.edu/~ronkowit/eliza.html
• The following passage shows Eliza’s talking to a teenage girl. Blue text Eliza’s
response
Men are all alike.
IN WHAT WAY
They're always bugging us about something or other.
CAN YOU THINK OF A SPECIFIC EXAMPLE

• Well, my boyfriend made me come here.
• YOUR BOYFRIEND MADE YOU COME HERE
• He says that I am depressed all the time.
• I AM SORRY TO HEAR THAT YOU ARE
DEPRESSED
• It's true that I am unhappy
• DO YOU THINK COMING HERE WILL HELP
YOU NOT TO BE UNHAPPY
• I need help, that seems certain.
• WHAT WOULD IT MEAN IF YOU GOT SOME
HELP
• Perhaps I could learn to get along with my mother.
• TELL ME MORE ABOUT YOUR FAMILY
• My mother takes care of me.
• WHO ELSE IN YOUR FAMILY TAKES CARE OF
YOU
• My father.

• The main characteristics of Eliza are:
• Simulation of Intelligence-They are not intelligent at all in real sense. They do not
understand the meaning of utterance. Instead these programs simulate intelligent
behaviour quite effectively by recognizing keywords and phrases. By using a table
lookup one of the few ways of responding question is chosen.
• Quality of Response- It is limited by the ways in which they can process the input text
at a syntactic level. There is a limitation on number of templates available.
• Coherence-The earlier version of the system imposed no structure on the
statement was based entirely on the current input and no context information
was used. More complex versions of Eliza can do a little better. So sense of
intelligence depends strongly on coherence of the conversation.
• Semantics – Such systems have no semantic representation of the content
for either user’s input or the reply. But it looks that it imitates the human
conversation style.

More definitions of AI
• Artificial intelligence (AI) is the theory and development of computer
systems capable of performing tasks that historically required
human intelligence, such as recognizing speech, making decisions, and
identifying patterns.
• It involves the development of algorithms and computer programs that
can perform tasks that typically require human intelligence such as
visual perception, speech recognition, decision-making, and language
translation.
• AI is relevant to any intellectual task; it is truly a universal field
• AI is an umbrella term that encompasses a range of technologies,
including machine learning, deep learning, and Natural Language
Processing(NLP)

Importance of A.I? Why should we learn?
• With the help of A.I, we can create a software or device which
can solve real world problem very easily with accuracy such as
marketing etc.
• We can create personal virtual assistant.(text and voice
enabled)
• We can build robots which can work in environment where
humans have risk.
• Opens a path for new technology and new opportunities.

Categorization of Intelligent Systems
• Intelligent systems are technologically advanced machines that perceive and respond to
the world around them.
• Ex: automated vacuums such as the Roomba,facial recognition programs,Amazon's
personalized shopping suggestions.
• An intelligent system is an advanced computer system that can gather, analyze and
respond to the data it collects from its surrounding environment.
• In order to design intelligent systems, it is important to categorize them into four
categories (Luger and Stubberfield 1993), (Russell and Norvig, 2003)
1. Systems that think like humans
2. Systems that think rationally
3. Systems that act like humans
4. Systems that act rationally
• Historically, all four approaches to AI have been followed, each by different people with
different methods. Let us look at the four approaches in more detail:

Thinking humanly (The cognitive modeling approach):
• The idea behind this approach is to determine whether the computer
thinks like a human.
• Most of the time it is a black box where we are not clear about our thought process.
• If we are going to say that a given program thinks like a human, we must
have some way of determining how humans think i.e., we need to get
inside the actual workings of human minds.
• There are three ways we can understand how we think:
• through introspection—trying to catch our own thoughts as they go by;
• through psychological experiments—observing a person in action; and
• through brain imaging—observing the brain in action.
• Requires scientific theories of internal activities of the brain.
• Once we have a sufficiently precise theory of the mind, it becomes
possible to express the theory as a computer program.

It is an area of cognitive science.
The interdisciplinary field of cognitive science brings together
computer models from AI and experimental techniques from
psychology to construct precise and testable theories of the
human mind.
One has to know functioning of brain and its mechanism for
possessing information. Neural network is a computing model
for processing information similar to brain.

Acting humanly (The Turing Test approach):
• In 1950, Alan Turing introduced a test in his 1950 paper, "Computing Machinery
and Intelligence," which considered the question, "Can Machine think?"
• This test is to check whether a machine can think like a human or not, this test is
known as the Turing Test.
• Turing proposed that the computer can be said to be an intelligent if it can mimic
human response under specific conditions.
• It provides a satisfactory operational definition of intelligence i.e., operational
test for intelligent behavior.
• The overall behavior of the system should be human like.
• The ideology behind this approach is that a computer passes the test if a
human interrogator, after asking some written questions, cannot identify
whether the written responses come from a human or from a computer.
• A computer would really be intelligent if it passed.

Contd..
Programming a computer to pass a rigorously applied test provides plenty to work on.
• The computer would need to possess the following capabilities:
• NATURAL LANGUAGE PROCESSING(NLP) : to enable it to communicate successfully
in English;
• KNOWLEDGE REPRESENTATION : to store what it knows or hears;
• 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.
Total Turing Test – requires interaction with objects and people in the real world. To pass the
total Turing Test, the computer will need
• COMPUTER VISION : to perceive objects i.e., to recognize the interrogator actions and
other objects during a test, and
• ROBOTICS : to manipulate objects and move about i.e., to act upon objects if requested.
• Note: These six disciplines compose most of AI

• “A system is said to have passed Turing test if a human
questioner is unable to determine from repeated questions of
any kind, whether he or she is talking to another person or to
a machine/system”.
• The very first so-called intelligent system named ELIZA passed
the Turing test which was written by Joseph Weizenbaum
during the period from 1964 to 1966.

Thinking rationally(The “laws of thought” approach):
• The idea behind this approach is to determine whether the computer thinks
rationally i.e. with logical reasoning.
• Rational behaviour means doing right thing.
• Such systems rely on logic rather than human to measure correctness.
• For thinking rationally or logically, logical formulas and theories are used for
synthesizing outcomes.
• Aristotle was one of the first to attempt to codify ― right thinking, that is, reasoning
processes.
• His syllogisms provided patterns for argument structures that always yielded
correct conclusions when given correct premises(evidences or principles).
• Eg. Socrates is a man; all men are mortal; therefore, Socrates is mortal. – logic
• These laws of thought were supposed to govern the operation of mind; their study
initiated the field called logic.

Contd..
• Not all intelligent behavior is mediated by logical
deliberation.
• There are two main obstacles to this approach.
1. it is not easy to take informal knowledge and state it in
the formal terms required by logical notation, particularly when
the knowledge is less than 100% certain.
2. there is a big difference between solving problem in
principle and solving it in practice.

Acting rationally (The rational agent approach):
• The idea behind this approach is to determine whether the computer acts
rationally i.e. with logical reasoning.
• Rational behaviour means doing right thing. Even if method is illogical, the
observed behaviour must be rational.
• An agent is just something that acts.
• A rational agent is one that acts so as to achieve the best outcome or,
when there is uncertainty, the best expected outcome.
• All computer programs do something, but computer agents are expected
to do more: operate autonomously, perceive their environment, persist
over a prolonged time period, and adapt to change, and create and
pursue goals.
• Maximized expected performance(based on the given information). Ex:
self driving car

Contd..
• In the “laws of thought” approach to AI emphasis was on correct
inferences.
• Focus is on systems that act sufficiently if not optimally in all
situations.
• Goal is to develop systems that are rational and sufficient.

Components of AI program
• An AI program should have knowledge base, navigational capability
which contains control strategy and inference mechanism.
• Knowledge base- AI should be learning in nature and update its
knowledge accordingly.
Facts & rules-Voluminous, imprecise & incomplete, dynamic.
• Control strategy- It determines which rule to be applied.
• Inference mechanism- It requires search through knowledge base
and derive new knowledge using existing knowledge with help of
inference rules.

The foundation of AI
• Commonly used AI techniques and theories are rule
based ,fuzzy logic, neural networks, decision theory, statistics ,
probability theory , genetics algorithms , etc.

Foundations of AI
• Since AI is interdisciplinary(relating to more than one branch of
knowledge) in nature, foundations of AI are in various fields.
• The disciplines that contributed ideas, viewpoints, and techniques to AI
are:
• Philosophy
• Mathematics and statistics
• Economics
• Neuroscience
• Psychology
• Computer engineering
• Control theory and Cybernetics
• Linguistics

Philosophy:
• It is the very basic foundation of AI. It address the following questions:
– Where does the knowledge come from?
– How does the knowledge lead to action?
– How does the mind arise from a physical brain?
– Can the formal rules be used to draw valid conclusions?
• The study of fundamental nature of knowledge, reality and existence are
considered for solving a specific problem is a basic thing in Artificial
Intelligence. e.g., foundational issues (can a machine think?), issues of
knowledge and believe.
• Aristotle (384–322 B.C.), was the first to formulate a precise set of
laws governing the rational part of the mind.
• Thomas Hobbes (1588–1679) proposed that reasoning was like
numerical computation, that “we add and subtract in our silent
thoughts.”

Contd..
• Ren´e Descartes (1596–1650): Developed dualistic theory
of mind and matter i.e., gave the first clear discussion of the
distinction between mind and matter(human soul and body).
• The confirmation theory of Carnap and Carl Hempel (1905–
1997) attempted to analyze the acquisition of knowledge
from experience.
• The final element in the philosophical picture of the mind is
the connection between knowledge and action. This
question is vital to AI because intelligence requires action as
well as reasoning

Mathematics and Statistics
• Philosophers staked out some of the fundamental ideas of AI,
but the leap to a formal science required a level of
mathematical formalization in three fundamental areas: logic,
computation, and probability
• AI required Formal Logic, Boolean logic, fuzzy logic and
Probability for planning and learning.
• What are the formal rules to draw valid conclusions?
• What can be computed?
• How do we reason with uncertain information?
• In AI, mathematics and statistics are most important for proving
theorems, writing algorithms, computation, modelling
uncertainty, learning from data

Contd..
• In 1879, Gottlob Frege (1848–1925) extended Boole’s logic to
include objects and relations, creating the first order logic ,that is
used today as the most basic knowledge representation system.
• Knowledge in formal representation are most required for writing
actions for agents.
• Alfred Tarski(1902-1983) introduced theory of reference , that
shows how to relate the objects in a logic to objects in the real
world.Computation required for analysing relation, and
implementation.
• The first nontrivial algorithm is thought to be Euclid’s algorithm for
computing greatest common divisors.

Contd..
• Besides logic and computation, the third great
contribution of mathematics to AI is the theory of
probability.
• The theory of probability can be seen as generalizing
logic to situations with uncertain information.
• Thomas Bayes (1702–1761), proposed a rule for
updating probabilities in the light of new evidence.
Bayes’ rule underlies most modern approaches to
uncertain reasoning in AI systems where we don’t know
expected outcomes i.e., to predict .

Economics
• How should we make decisions in accordance with our preferences?
• How should we do this when others may not go along?
• How should we do this when the payoff may be far in the future?
• i.e., Deals with investing the amount of money, and Maximization of
utility with minimal investment.
• While developing an AI product, we should make decisions for:
• When to invest?
• How to invest?
• How much to invest? And
• Where to invest?
• To answer these questions one should have knowledge about Decision
Theory, Game Theory, Operation Research, and etc.

Neuro Science
• How do brains process information?
• It is the study of the nervous system, particularly the human brain. Human
brains are somehow different, when compared to other creatures.
• e.g., brain architecture. The brain consisted largely of nerve cells, or
neurons and the observation of individual neurons can lead to thought,
action, and consciousness of one’s brain.
• Nicolas Rashevsky (1936, 1938) was the first to apply mathematical
models to the study of the nervous system.
• The measurement of intact brain activity began in 1929 with the invention
by Hans Berger of the Electroencephalograph (EEG).
• The recent development of functional magnetic resonance imaging (fMRI)
(Ogawa et al., 1990; Cabeza and Nyberg, 2001) is giving neuroscientists
unprecedentedly detailed images of brain activity, enabling measurements
that correspond in interesting ways to ongoing cognitive processes.

Psychology and Cognitive Science
• How do humans and animals think and act?
• Problem solving skills i.e., how humans will take decisions in complicated
situation.
• How do people behave in unexpected situation?
• Perceive (how do they observe the environment to solve a particular
problem)
• Process cognitive information and represent knowledge.
• The behaviorism movement, led by John Watson (1878–1958).
Behaviorists insisted on studying only objective measures of the percepts
(or stimulus) given to an animal and its resulting actions (or response).
Behaviorism discovered a lot about rats and pigeons but had less
success at understanding humans.

Contd..
• Cognitive psychology, which views the brain as an information-
processing device. (Cognition is a process of acquiring knowledge
and understanding through thought, experience and the senses)

Computer Science & Engineering
• How can we build fast and efficient computer?
• Logic and inference theory, algorithms, programming
languages, and system building are important parts of
Computer Science.
• For artificial intelligence to succeed, we need two things:
intelligence and an artifact. The computer has been the
artifact of choice.
• The first operational computer was the electromechanical
Heath Robinson, built in 1940 by Alan Turing’s team for a
single purpose: deciphering German messages.
• The first operational programmable computer was the Z-3, the
invention of Konrad Zuse in Germany in 1941.

Contd..
• Computer hardware gradually changed for AI applications, such
as the graphics processing unit(GPU), tensor processing
unit(TPU), and wafer scale engine(WSE)
• The amount of computing power used to train top machine
learning applications and the utilization doubled every 100 days.
• The super computers and quantum computers can solve very
complicated AI problems.
• The software side of computer science, supplied the
operating systems, programming languages, and tools needed
to write modern programs.

Control theory
• How can artifacts operate under their own control?
• It helps the system to analyze, define, debug and fix errors by itself.
• Ktesibios of Alexandria (c. 250 B.C.) built the first self-controlling
machine: a water clock with a regulator that maintained a constant
flow rate. This invention changed the definition of what an artifact
could do.
• Developing self-regulating feedback control systems, the
thermostat and the submarine are some examples of control theory.
• By using control theory, robots are created that fix all the errors by
itself

Linguistics
• How does language relate to thought?
• Understanding language requires an understanding of the subject matter and
context, not just an understanding of the structure of sentences.
• In 1957, B. F. Skinner published Verbal Behavior. This was a comprehensive,
detailed account of the behaviorist approach to language learning, written by
the foremost expert in the field.
• Noam Chomsky, who had just published a book on his own theory, Syntactic
Structures. pointed out that the behaviorist theory did not address the notion of
creativity in language—it did not explain how a child could understand and make
up sentences that he or she had never heard before.
• Modern linguistics and AI, were “born” at about the same time, and grew up
together, intersecting in a hybrid field called computational linguistics or
natural language processing.
• Languages and thoughts are believed to be tightly intertwined

Contd...
• Much of the early work in knowledge representation (the study of
how to put knowledge into a form that a computer can reason with)
was tied to language and informed by research in linguistics.
• Speech demonstrates so much of human intelligence. Speech
recognition is a technology which enables a machine to understand
the spoken language and translate into a machine readable format.
• It is a way to talk with a computer, and on the basis of that command,
a computer can perform a specific task
• It includes speech to text, text to speech.

History of AI
• Artificial Intelligence is not a new word and not a new technology
for researchers. This technology is much older than you would
imagine. Even there are the myths of Mechanical men in Ancient
Greek and Egyptian Myths.
• The contribution of other fields to the development of AI is
seen by many as so important that they consider the
history of AI can’t be recounted without including the
discussion of history of knowledge that dates back to
Aristotle.

History of AI Phases
• The inception of Artificial Intelligence(1943-1955)
• The birth of Artificial Intelligence(1956)
• Early enthusiasm, great expectations(1952-1969)
• A dose of reality(1966-1973)
• Knowledge-based systems: The key to power? (1969-1979)
• AI becomes an industry(1980-present)
• The return of neural networks(1986-present)
• AI becomes a science(1987-present)
• The emergence of intelligent agents(1995-present)
• The availability of very large data sets (2001–present):
• Deep learning and artificial general intelligence (2011-present)

The inception/gestation of Artificial Intelligence
(1943-1952):
 1943: The first work which is now recognized as AI was
done by Warren McCulloch and Walter pits.
• They proposed a model of artificial neurons(each neuron characterized
by on or off, ‘on’ occurs in response to simulation by a sufficient number
of neighbouring neurons)
• They showed that any computable function could be computed by some
network of connected neurons
 1949: Donald Hebb demonstrated a simple updating rule for modifying the
connection strengths between neurons. His rule is now called Hebbian
learning, remains an influential model to this day.
• 1950: Alan Turing publishes "Computing Machinery and Intelligence" in
which he proposed a test. He also introduced machine learning, genetic
algorithms and reinforcement learning

The birth of Artificial Intelligence (1956)
 1955: Allen Newell and Herbert A. Simon presented the most mature
work, a mathematical theorem – proving system called the "Logic
Theorist“(the "first artificial intelligence program “ ). This program had
proved 38 of 52 Mathematics theorems, and find new and more elegant
proofs for some theorems.
 1956: The word "Artificial Intelligence" first adopted by American
Computer scientist John McCarthy at the Dartmouth Conference, the
first
conference devoted to the subject. For the first time, AI coined as an
academic field.

The golden years-Early enthusiasm, great expectations
(1956-1974)
• 1958: John McCarthy (MIT) invented the high-level language Lisp(AI
programming language), acronym for List Processing, the first
programming language for AI research
• 1959: Arthur Samuel (IBM) wrote the first game-playing program, for
checkers, to achieve sufficient skill to challenge a world champion. He
disproved the idea that computers can do only what they are told to: his
program quickly learned to play a better game than its creator.
•1966: The researchers emphasized developing algorithms which can solve
mathematical problems. Joseph Weinbaum created the first chatbot in 1966,
which was named as ELIZA. First so-called intelligent system, to converse
in English.
•1972: The first intelligent humanoid robot was built in Japan which was
named as WABOT-1.

The first AI winter (1974-1980):
 AI winter refers to the time period where computer scientist
dealt with a severe shortage of funding from government for AI
researches.
• During AI winters, an interest of publicity on artificial
intelligence was decreased

A boom of AI (1980-1987):
 Year 1980: After AI winter duration, AI came back with "Expert
System". Expert systems were programmed that emulate the
decision-making ability of a human expert.
 In the Year 1980, the first national conference of the American Association
of Artificial Intelligence was held at Stanford University.
The second AI winter (1987-1993):
 Again Investors and government stopped in funding for AI research as
due to high cost but not efficient result. The expert system such as XCON
was very cost effective.

The emergence of intelligent agents (1993-2011):
• In 1997- IBM Deep Blue beats the World Chess Champion Kasparov
and became the first computer to beat a world chess champion
 2002: for the first time, AI entered the home in the form of Roomba, a
vacuum cleaner. iRobot, founded by researchers at the MIT Artificial
Intelligence Lab, introduced Roomba. By 2006, two million had been sold.
 2006: AI came in the Business world till the year 2006. Companies like
Facebook, Twitter, and Netflix also started using AI.

Deep learning and AGI (2011-present)
 The term deep learning refers to machine learning using
multiple layers of simple, adjustable computing elements.
 2011: In the year 2011, IBM's Watson won jeopardy, a quiz show, where
it had to solve the complex questions as well as riddles. Watson had
proved that it could understand natural language and can solve tricky
questions quickly.
• Year 2012: Google has launched an Android app feature "Google now", which
was able to provide information to the user as a prediction.
 2014: In the year 2014, Chatbot "Eugene Goostman" won a competition
in the infamous "Turing test.“
 2018: The "Project Debater" from IBM debated on complex topics with
two master debaters and also performed extremely well.

History of AI contd..
• Following are some milestones in the history of AI which defines the
journey from the AI generation to till date development.

Sub areas of AI
• Artificial Intelligence is an interdisciplinary area having
various sub fields in its domain.
• Each one of these field is an area of research in AI itself.
• All the Sub-Fields can be distinguished as per various
techniques.
• Some of them are:

Machine Learning and Deep learning
• The capability of Artificial Intelligence systems to learn by extracting patterns
from data is known as Machine Learning
• Machine learning is a part of AI which provides intelligence to machines with the
ability to automatically learn with experiences without being explicitly programmed.
• It is primarily concerned with the design and development of algorithms that allow
the system to learn from historical data.
• Machine Learning is based on the idea that machines can learn from past data,
identify patterns, and make decisions using algorithms.
• Machine learning algorithms are designed in such a way that they can learn and
improve their performance automatically
• Deep Learning is a subfield of Machine Learning that involves the
use of neural networks to model and solve complex problems.

Neural Networks
• Neural Networks are inspired by human brains and copies the
working process of human brains.
• It is based on a collection of connected units or nodes called
artificial neurons or perceptrons.
• The Objective of this approach was to solve the problems in the
same way that a human brain does

Computer Vision
• In Artificial Intelligence, Vision (Visioning Applications) means processing
any image/video sources to extract meaningful information and take
action based on that.
• In this field of artificial Intelligence we have also developed such kind of
robots which are acquiring human activities within some days or
sometimes some hours and train themselves . For e.g. object recognition,
image understanding etc.
• Face recognition programs

Natural language Processing
• Natural Language Processing(NLP) is a part of
Computer Science, Human language, and Artificial
Intelligence.
• It is the technology that is used by machines to
understand, analyze, manipulate, and interpret
human's languages.
• AltaVista's translation of webpages

Speech Processing / Speech Recognition
• Speech Processing / Recognition is the ability of a computer
and a program to identify words and phrases in the spoken
language and convert them to machine readable format.
• The real life examples of Speech processing are Google
Assistant, Amazon Alexa and Apple’s Siri Application etc.
• PEGASUS, a spoken language interface for on-line air
travel planning to book a flight can have the entire
conversation guided by an automated speech recognition
and dialog management system

Robotics
• Robots are the artificial agents
• A robot is a machine capable of sensing and interacting
with its environment.
• It behaves like human and build for the purpose of manipulating the
objects by perceiving, picking, moving, modifying the physical
properties of object, or to have an effect thereby freeing manpower
from doing repetitive functions without getting bored, distracted, or
exhausted.

Knowledge representation models
• Knowledge representation and reasoning (KR, KRR) is the part of Artificial intelligence which
concerned with AI agents thinking and how thinking contributes to intelligent behavior of
agents.
• Field of artificial intelligence on representing information in a form that a computer
system can use to solve complex tasks such as diagnosing a medical condition or
having a dialog in a natural language.
• Examples of knowledge representation formalisms include semantic nets(represents
semantic relations between concepts in a network), systems architecture.
• There are mainly four ways of knowledge representation which are given as follows:
• Logical Representation
• Semantic Network Representation
• Frame Representation
• Production Rules

• Logical Representation: Logical representation is one of the most formal and
rigorous methods for knowledge representation in AI. It uses formal logic to
encode knowledge in a way that allows for precise and unambiguous reasoning.
• Semantic Networks: As discussed earlier, semantic networks are a graphical way
of representing knowledge, where concepts are nodes and relationships are
edges. They are highly intuitive and are often used to represent hierarchical and
associative relationships.
• Production Rules: Production rules are another popular method of knowledge
representation in AI, particularly in expert systems. These rules are expressed as
"if-then" statements that define actions to be taken when certain conditions are
met.
• Frames Representation: Frames are data structures that capture stereotypical
knowledge about objects, situations, or events, similar to how objects are used in
object-oriented programming. A frame consists of a set of attributes (slots) and
their associated values

Expert system
• It is considered at the highest level of human intelligence and
expertise.
• The purpose of an expert system is to solve the most complex
issues in a specific domain.
• A system is considered to be an expert when it comes to solving
problems or giving advice in some knowledge-rich domain.
• An expert system is AI software that uses knowledge stored in a
knowledge base to solve problems that would usually require a
human expert thus preserving a human expert’s knowledge in its
knowledge base.

Some Expert Systems:
• MYCIN, this expert system was developed for assisting physicians in the treatment
of blood infections in humans.
• Diagnostic Systems: for medical diagnosis of illness , diagnosing bacterial
infections of the blood and suggesting treatments.
• DENDRAL – used in chemical mass spectroscopy to identify chemical constituents
• DIPMETER – geological data analysis for oil
• PROSPECTOR – geological data analysis for minerals
• CaDeT: The CaDet expert system is a diagnostic support system that can detect
cancer at early stages.
• PXDES: It is an expert system that is used to determine the type and level of lung
cancer.

Autonomous planning and Scheduling
• A hundred million miles from Earth, NASA’s Remote Agent
program became the first on-board autonomous planning
program to control the scheduling of operations for a
spacecraft.
• The Dynamic Analysis and Replanning Tool(DART) is
an artificial intelligence program[1]
used by the U.S.
military to do automated logistics of planning and scheduling
for transportation.
• Ride hailing companies like Uber and mapping services like
Google maps provide driving directions, quickly plotting
optimal route

Common sense reasoning dealing with
uncertainty and decision making

Other sub areas:
• Theorem proving mechanisms: also known as an automated proving
system, systems are deﬁned and speciﬁed by users in an appropriate
mathematical logic. Mathematical Theorem Proving Use inference methods to
prove new theorems.
• Data Mining : is a process used by organizations to extract specific data from
huge databases to solve business problems. It primarily turns raw data into
useful information
• Web agents: are AI-driven software that can autonomously access, navigate,
and interact with the internet. They are designed to perform specific tasks,
such as gathering, analyzing, and processing data from various sources.
• Game playing methodologies.
• Models for intelligent tutoring systems.

Applications of AI
• AI finds applications in almost all areas of real-life
applications.
• Broadly speaking, business, engineering, medicine, education
and manufacturing are the main areas.
• AI has the potential to revolutionize many industries and has a
wide range of applications given below:

Applications of AI contd..
• Business: financial strategies, give advice.
• Business Analytics: Customer behavior modelling, customer
segmentation, fraud propensity, market research, market structure, and
models for attrition(gradually reducing strength or effectiveness), default,
purchase, and renewals, to predict stock market price
• Banking: Credit card attrition, credit and loan application evaluation,
Fraud detection, fraud and risk evaluation, and loan delinquencies.
• Financial: Corporate bond ratings, corporate financial analysis, credit line
use analysis, currency price prediction, loan advising, mortgage
screening, real estate appraisal, and portfolio trading
• Spam Detection: Spam detection is used to detect unwanted e-mails
getting to a user's inbox.

Contd..
• Engineering: solve problems in engineering, check design, offer
suggestions to create new product, expert systems for all engineering
problems
• Self-driving cars: It enables your car to steer, accelerate and brake
automatically within its lane. It requires sensors like camera for object
detection
• Industry: Controlling water purification plants, Handling problems in
constraint satisfaction in structural design, Pattern analysis for quality
assurance, tackling sludge waste water treatment(using fuzzy logic).
• Designing and Manufacturing: assembly, inspection and maintenance. It can be
broadly used for designing and manufacturing physical devices such as camera
lenses and automobiles.

Contd..
• Medical: monitoring, diagnosing and prescribing. Cancer cell analysis,
ECG and EEG analysis, emergency room test advisement, expense
reduction and quality improvement for hospital systems. Controlling
arterial pressure when providing anaesthesia to patients.
• Transportation systems: Handling underground train operations,
Controlling train schedules, Braking and stopping vehicles based on
parameters, such as car speed, acceleration and wheel speed. Routing
systems, truck brake diagnosis systems, and vehicle scheduling.
• Diagnosis Systems: to assume cause of disease from observed data,
conduction medical operations on humans. Used in diagnostic radiology
and diagnostic support systems , Diagnosis of prostate cancer and
diabetes
• Gaming: AI plays crucial role in strategic games such as chess, poker,
tic-tac-toe, etc., where machine can think of large number of possible
positions

Contd..
• Education: in teaching. Adaptive learning software, dynamic forecasting,
education system analysis and forecasting, student performance
modeling.
• Defense: Counterterrorism, facial recognition, Object identification, object
discrimination, sensors, sonar, radar and image signal processing,
signal/image identification, target tracking, and weapon steering. Locating
and recognizing targets underwater, Supports naval decision making,
Using thermal infrared images for target recognition.
• Space shuttle scheduling.
• Autonomous planning and scheduling. DART
• Mining: used when conditions are dangerous
• Robotic vehicles. Waymo
• Legged locomotion. BigDog(a quadruped robot), Atlas(humanoid robot-
not only walks on uneven terrain, but jumps, backflips)

Contd..
• Machine translation. Enables the reading of documents in over 100
languages
• Speech recognition.
• E-commerce : shopping etc.
• Agriculture and Farming : prune trees & selectively harvest mixed crops
• Recommendations. Recommend what you might like based on your past
experiences and those of others like you.
• Social Media: Sites such as Facebook, Twitter, and Snapchat contain
billions of user profiles, which need to be stored and managed in a very
efficient way. AI can organize and manage massive amounts of data. AI
can analyze lots of data to identify the latest trends, hashtag, and
requirement of different users.

Contd..
• Game playing. Deep Blue, ALPHAZERO(able to learn
through self play)
• Image understanding.
• Medicine. AI algorithms, now equal or exceed expert doctors
diagnosing many conditions, particularly when diagnosing
through images. LYNA system achieves 99.6% accuracy in
diagnosing metastatic breast cancer
• Climate science. Discovers detailed extreme weather
events, use supercomputer with specialized GPU.

AI_Unit_IARTIFICIAL INTELLIGENCE UNIT 1 OSMANIA UNIVERSITY

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