GOVERNMENT ENGINEERING

COLLEGE BHARATPUR

(SHYORANA SEWAR NH-11 BHARATPUR , RAJASTHAN )

WEBSITE- www.ecbharatpur.ac.in

Name- Muskan Singh

Roll No. - 20EELAD002
Year – 2nd / Semester- 3rd
Duration – 15 Days
Industrial Training From CENTRE FOR
ELECTONIC GOVERNANCE in Fundamental of
AI
 INDEX
1) Basic Introduction of AI
2) Intelligent Agents in AI
3) Problems solving by Searching
4) Uninformed search
5) Informed search
6) Searching
7) Shorting
8) Adversarial search

ACKNOWLEDGEMENT
“Gratitude is not a thing of expression , it is more
matter of feeling”
There is always a sense of gratitude which one
expression towards other for their and supervision in
achieving the goals. This formal piece of
acknowledgment is an attempt to express the feeling
 of gratitude towards people who helpful me in
successfully competing of my training.

I would like to express my deep gratitude to Mr.

Sumit Kumar who helps me a lot. He guide me and
give valuable Suggestions throughout the Pursuance
of this research Project.

Above all no words can express my feelings. I want

to thanks all who support and cooperate me a lot in
collecting necessary information.

BASIC INTRODUCTION OF AI
“The science and engineering of making intelligent
machines, especially intelligent computer programs”.
-John McCarthy-

Artificial Intelligence is an approach to make a

computer, a robot, or a product to think how smart
human think. AI is a study of how human brain think,
learn, decide and work, when it tries to solve problems.
And finally this study outputs intelligent software
systems.The aim of AI is to improve computer functions
which are related to human knowledge, for example,
reasoning, learning, and problem-solving.

The intelligence is intangible. It is composed of

 Reasoning
 Learning
 Problem Solving
 Perception
 Linguistic Intelligence

The objectives of AI research are reasoning, knowledge

representation, planning, learning, natural language
processing, realization, and ability to move and
manipulate objects. There are long-term goals in the
general intelligence sector.
Approaches include statistical methods, computational
intelligence, and traditional coding AI. During the AI
research related to search and mathematical
optimization, artificial neural networks and methods
based on statistics, probability, and economics, we use
many tools. Computer science attracts AI in the field of
science, mathematics, psychology, linguistics,
philosophy and so on.
Maturation of Artificial Intelligence (1943-1952)
o Year 1943: The first work which is now recognized
as AI was done by Warren McCulloch and Walter
pits in 1943. They proposed a model of artificial
neurons.
o Year 1949: Donald Hebb demonstrated an
updating rule for modifying the connection
strength between neurons. His rule is now
called Hebbian learning.
o Year 1950: The Alan Turing who was an English
mathematician and pioneered Machine learning in
1950. Alan Turing publishes "Computing
Machinery and Intelligence" in which he proposed
a test. The test can check the machine's ability to
exhibit intelligent behavior equivalent to human
intelligence, called a Turing test.
The birth of Artificial Intelligence (1952-1956)
o Year 1955: An Allen Newell and Herbert A. Simon
created the "first artificial intelligence
program"Which was named as "Logic Theorist".
This program had proved 38 of 52 Mathematics
theorems, and find new and more elegant proofs
for some theorems.
o Year 1956: The word "Artificial Intelligence" first
adopted by American Computer scientist John
 McCarthy at the Dartmouth Conference. For the
first time, AI coined as an academic field.
At that time high-level computer languages such as
FORTRAN, LISP, or COBOL were invented. And the
enthusiasm for AI was very high at that time.
The golden years-Early enthusiasm (1956-1974)
o Year 1966: The researchers emphasized
developing algorithms which can solve
mathematical problems. Joseph Weizenbaum
created the first chatbot in 1966, which was
named as ELIZA.
o Year 1972: The first intelligent humanoid robot
was built in Japan which was named as WABOT-1.
The first AI winter (1974-1980)
o The duration between years 1974 to 1980 was the
first AI winter duration. AI winter refers to the
time period where computer scientist dealt with a
severe shortage of funding from government for AI
researches.
o During AI winters, an interest of publicity on
artificial intelligence was decreased.
A boom of AI (1980-1987)
o 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.
o 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)
o The duration between the years 1987 to 1993 was
the second AI Winter duration.
o 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)
o Year 1997: In the year 1997, IBM Deep Blue beats
world chess champion, Gary Kasparov, and
became the first computer to beat a world chess
champion.
o Year 2002: for the first time, AI entered the home
in the form of Roomba, a vacuum cleaner.
o Year 2006: AI came in the Business world till the
year 2006. Companies like Facebook, Twitter, and
Netflix also started using AI.
o

INTELLIGENT AGENTS IN AI
What is an Intelligent Agent (IA)?
This agent has some level of autonomy that allows it to
perform specific, predictable, and repetitive tasks for
users or applications.
It’s also termed as ‘intelligent’ because of its ability to
learn during the process of performing tasks.
The two main functions of intelligent agents include
perception and action. Perception is done through
sensors while actions are initiated through actuators.
Intelligent agents consist of sub-agents that form a
hierarchical structure. Lower-level tasks are performed
by these sub-agents.
The higher-level agents and lower-level agents form a
complete system that can solve difficult problems
through intelligent behaviors or responses.

Characteristics of intelligent agents

Intelligent agents have the following distinguishing
characteristics:

 They have some level of autonomy that allows

them to perform certain tasks on their own.
  They have a learning ability that enables them to
learn even as tasks are carried out.
 They can interact with other entities such as
agents, humans, and systems.
 New rules can be accommodated by intelligent
agents incrementally.
 They exhibit goal-oriented habits.
 They are knowledge-based. They use knowledge
regarding communications, processes, and
entities.

PROBLEMS SOLVING BY SEARCHING

Search algorithms are one of the most important areas

of Artificial Intelligence. This topic will explain all about
the search algorithms in AI.
Problem-solving agents:
In Artificial Intelligence, Search techniques are
universal problem-solving methods. Rational
agents or Problem-solving agents in AI mostly used
these search strategies or algorithms to solve a specific
problem and provide the best result. Problem-solving
agents are the goal-based agents and use atomic
representation. In this topic, we will learn various
problem-solving search algorithms.
Search Algorithm Terminologies:

Search: Searchingis a step by step procedure to solve a

search-problem in a given search space. A search
problem can have three main factors:
1. Search Space: Search space represents a set
of possible solutions, which a system may
have.
2. Start State: It is a state from where agent
begins the search.
3. Goal test: It is a function which observe the
current state and returns whether the goal
state is achieved or not.
o Search tree: A tree representation of search
problem is called Search tree. The root of the
search tree is the root node which is
corresponding to the initial state.
o Actions: It gives the description of all the available
actions to the agent.
o Transition model: A description of what each
action do, can be represented as a transition
model.
 o Path Cost: It is a function which assigns a numeric
cost to each path.
o Solution: It is an action sequence which leads from
the start node to the goal node.
o Optimal Solution: If a solution has the lowest cost
among all solutions.
Properties of Search Algorithms:
Following are the four essential properties of search
algorithms to compare the efficiency of these
algorithms:
Completeness: A search algorithm is said to be
complete if it guarantees to return a solution if at least
any solution exists for any random input.
Optimality: If a solution found for an algorithm is
guaranteed to be the best solution (lowest path cost)
among all other solutions, then such a solution for is
said to be an optimal solution.
Time Complexity: Time complexity is a measure of
time for an algorithm to complete its task.
Space Complexity: It is the maximum storage space
required at any point during the search, as the
complexity of the problem.
UNINFORMED SEARCH