1. ARTIFICIAL INTELLIGENCE
a Technical Paper submitted to
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
in partial fulfillment of the requirement
for the award of the degree of
BACHELOR OF TECHNOLOGY
in
ELECTRONICS AND COMMUNICATION ENGINEERING
by
G.KARTHIK
(H.T.No:12N01A0470)
Department of Electronics and Communication Engineering,
SREE CHAITANYA COLLEGE OF ENGINEERING
(Affiliated to JNTUH, HYDERABAD)
THIMMAPOOR, KARIMNAGAR, TS-505 527.
2012-2016
2. SREE CHAITANYA COLLEGE OF ENGINEERING,
THIMMAPOOR, KARIMNAGAR, TS-505 527
DEPARTMENT OF ELECTRONICS & COMMUNICATION ENGINEERING
[
CERTIFICATE
This is to certify that the Technical Seminar report entitled “ARTIFICIAL
INTELLIGENCE” is being submitted by G.KARTHIK bearing a 12N01A0470 in
partial fulfillment of the requirements for the award of the Degree of Bachelor of
Technology in Electronics and Communication Engineering, to the Jawaharlal
Nehru Technological University Hyderabad, is a bonafide work carried out by
him/her under my guidance and supervision.
The result embodied in this report has not been submitted to any other
University or Institution for the award of any degree or diploma.
Supervisor Head of the Department
Sri. S.SRIKANTH, Sri. M. RAJU,
Assistant Professor, Associate Professor,
Department of ECE, Department of ECE,
Sree Chaitanya College of Sree Chaitanya College of
Engineering. Engineering.
3. ACKNOWLEDGEMENTS
The Satisfaction that accomplishes the successful completion of any task would
be incomplete without the mention of the people who make it possible and whose
constant guidance and encouragement crown all the efforts with success.
It is my privilege and pleasure to express my profound sense of respect, gratitude
and indebtedness to my supervisor Sri. S.SRIKANTH, Assistant Professor,
Department of ECE, SCCE, for his constant guidance, inspiration, and constant
encouragement throughout this Technical Seminar work.
I wish to express our deep gratitude to Sri. M. RAJU, Associate
Professor and HOD, Department of ECE, SCCE, karimnagar for his cooperation and
encouragement, in addition to providing necessary facilities throughout the Technical
seminar work
I sincerely extend my thanks to Dr.R.V.R.K.CHALAM, Principal, SCCE,
Karimnagar, for providing all the facilities required for completion of this Seminar.
I would like to thank all the staff and all my friends for their good wishes, their
helping hand and constructive criticism, which led the successful completion of this
Technical Seminar.
I am immensely indebted to our parents, brothers and sisters for their love and
unshakable belief in us and the understanding and ever-decreasing grudges for not
spending time more often. I will now, since the excuse is in the process of vanishing by
being printed on these very pages.
Finally, I thank all those who directly and indirectly helped me in this regard. I
apologize for not listing everyone here.
G.KARTHIK
4. DECLARATION
I here by declare that the work which is being presented in this dissertation entitled,
“ARTIFICIAL INTELLIGENCE”, submitted towards the partial fulfillment of
the requirements for the award of the degree of Bachelor of Technology in Electronics
and Communication Engineering, S.C.C.E, Karimnagar, is an authentic record of my
own work carried out under the supervision of Sri. S.SRIKANTH, Assistant Professor,
Department of ECE, S.C.C.E, Karimnagar.
To the best of our knowledge and belief, this Technical Seminar bears no
resemblance with any report submitted to SCCE or any other University for the award of
any degree or diploma.
Date: G.KARTHIK
5. Place:
iv
ABSTRACT
This paper is the introduction to Artificial intelligence (AI). Artificial intelligence
is exhibited by artificial entity, a system is generally assumed to be a computer. AI
systems are now in routine use in economics, medicine, engineering and the military, as
well as being built into many common home computer software applications, traditional
strategy games like computer chess and other video games. We tried to explain the brief
ideas of AI and its application to various fields. It cleared the concept of computational
and conventional categories. AI is used in typical problems such as Pattern
recognition, Natural language processing and more. This system is working throughout
the world as an artificial brain.
Intelligence involves mechanisms, and AI research has discovered how to make
computers carry out some of them and not others. We can learn something about how to
make machines solve problems by observing other people or just by observing our own
methods. On the other hand, most work in AI involves studying the problems the world
presents to intelligence rather than studying people or animals. AI researchers are free to
use methods that are not observed in people or that involve much more computing than
people can do. We discussed conditions for considering a machine to be intelligent. We
argued that if the machine could successfully pretend to be human to a knowledgeable
observer then you certainly should consider it intelligent.
8. vii
LIST OF FIGURES
FIG.NO FIGURE NAME PAGE.NO
Fig-2.1 Development of artificial intelligence 2
Fig-3.1 Internal structure of a neuron 6
Fig-3.2 Charts representing the logic of expert systems 7
Fig-3.3 Traditional software development process 8
Fig-3.4 Expert System development 9
Fig-3.5 Risk Management Process 10
9. Viii
CHAPTER-1
INTRODUCTION:-
Artificial Intelligence:-
Artificial intelligence is a branch of science which deals with helping machines
find solution to complex problems in a more human like fashion. This generally involves
borrowing characteristics from human intelligence, and applying them as
algorithms in a computer friendly way. A more or less or flexible or efficient approach
can be taken depending on the requirements established, which influences how artificial
intelligent behavior appears.
Artificial intelligence is generally associated with computer science, but it has
many important links with other fields such as maths, psychology, cognition , biology
and philosophy , among many others . Our ability to combine knowledge from all these
fields will ultimately benefits our progress in the quest of creating an intelligent artificial
being.
A.I is mainly concerned with the popular mind with the robotics development,
but also the main field of practical application has been as an embedded component in the
10. areas of software development which require computational understandings and
modeling such as such as finance and economics, data mining and physical science.
A.I in the fields of robotics is the make a computational models of human thought
processes. It is not enough to make a program that seems to behave the way human do.
You want to make a program that does it the way humans do it.
In computer science they also the problems because we have to make a computer
that are satisfy for understanding the high-level languages and that was taken to be A.I.
1
CHAPTER-2
HISTORY OF A.I.
Fig:2.1 development of Artificial Intelligence
The intellectual roots of AI, and the concept of intelligent machines, may be
found in Greek mythology. Intelligent artifacts appear in literature since then, with real
mechanical devices actually demonstrating behaviour with some degree of intelligence.
After modern computers became available following World War-II, it has become
possible to create programs that perform difficult intellectual tasks.
2.1 1950
11. With the development of the electronic computer in 1941 and the stored program
computer in 1949 the condition for research in artificial intelligence is given, still the
observation of a link between human intelligence and machines was not widely observed
until the late in 1950
The first working AI programs were written in 1951 to run on the Ferranti Mark I
machine of the University of Manchester (UK): a draughts-playing program written by
Christopher Strachey and a chess-playing program written by Dietrich Prinz.
The person who finally coined the term artificial intelligence and is regarded as
the father of the of AL is John McCarthy. In 1956 he organized a conference “the
Darthmouth summer research project on artificial intelligence" to draw the talent and
2
expertise of others interested in machine intelligence of a month of brainstorming. In the
following years AI research centers began forming at the Carnegie Mellon University as
well as the Massachusetts Institute of Technology (MIT) and new challenges were faced:
1) The creation of systems that could efficiently solve problems by limiting the search.
2) The construction of systems that could learn by themselves.
2.2 1960
By the middle of the 1960s, research in the U.S. was heavily funded by the
Department of Defense and laboratories had been established around the world. AI's
founders were profoundly optimistic about the future of the new field: Herbert Simon
predicted that "machines will be capable, within twenty years, of doing any work a man
can do" and Marvin Minsky agreed, writing that "within a generation .
By the 1960’s, America and its federal government starting pushing more for
the development of AI. The Department of Defense started backing several programs in
order to stay ahead of Soviet technology. The U.S. also started to commercially market
the sale of robotics to various manufacturers. The rise of expert systems also became
popular due to the creation of Edward Feigenbaum and Robert K. Lindsay’s DENDRAL.
DENDRAL had the ability to map the complex structures of organic chemicals, but like
12. many AI inventions, it began to tangle its results once the program had too many factors
built into it... the problem of creating 'artificial intelligence' will substantially be solved".
The same predicament fell upon the program SHRDLU which would use robotics
through a computer so the user could ask questions and give commands in English.
2.3 1980
In the early 1980s, AI research was revived by the commercial success of expert
systems, a form of AI program that simulated the knowledge and analytical skills of one
or more human experts. By 1985 the market for AI had reached over a billion dollars.
3
At the same time, Japan's fifth generation computer project inspired the U.S and
British governments to restore funding for academic research in the field. In the 1990s
and early 21st century, AI achieved its greatest successes, albeit somewhat behind the
scenes. Artificial intelligence is used for logistics, data mining, medical diagnosis and
many other areas throughout the technology industry
2.4 1990
From 1990s until the turn of the century, AI has reached some incredible
landmarks with the creation of intelligent agents. Intelligent agents basically use their
surrounding environment to solve problems in the most efficient and effective manner. In
1997, the first computer (named Deep Blue) beat a world chess champion. In 1995, the
VaMP car drove an entire 158 km racing track without any help from human intelligence.
In 1999, humanoid robots began to gain popularity as well as the ability to walk around
freely. Since then, AI has been playing a big role in certain commercial markets and
throughout the World Wide Web. The more advanced AI projects, like fully adapting
commonsense knowledge, have taken a back-burner to more lucrative industries.
13. 4
CHAPTER-3
MECHANISMS
Generally speaking AI systems are built around automated inference engines
including forward reasoning and backwards reasoning. Based on certain conditions ("if")
the system infers certain consequences ("then").
In terms of consequences AI comes under two categories:
1. Conventional AI
2. Computational intelligence
3.1 Conventional AI:
Conventional AI research focuses on attempts to mimic human intelligence
through symbol manipulation and symbolically structured knowledge bases.
Methods include in conventional AI are:
• Expert systems: An expert system can process large amounts of known information
and provide conclusions based on them.
• Case based reasoning: stores a set of problems and answers in an organized data
structure called cases. A case based reasoning system upon being presented with a
problem finds a case in its knowledge base that is most closely related to the new
14. problem and presents its solutions as an output with suitable modifications.
3.2 Computational intelligence:
Subject involved in Computational intelligence is Neural network.
Neural networks: systems with very strong pattern recognition capabilities.
Pattern recognition:
A complete pattern recognition system consists of a sensor that gathers the
observations to be classified or described; a feature extraction mechanism that computes
numeric or symbolic information from the observations and a classification or numeric or
5
symbolic information from the observations; and a classification or description scheme
using statistical (or decision theoretic) analysis that does the actual job of classifying or
describing observations, relying on the extracted features.
3.3 Neural networks and parallel computation :
The human brain is made up of a web of billions of cells called neurons, and
understanding its complexities is seen as one of the last frontiers in scientific research. It
is the aim of AI researchers who prefer this bottom-up approach to construct electronic
circuits that act as neurons do in the human brain. Although much of the working of the
brain remains unknown, the complex network of neurons is what gives humans
intelligent characteristics. By itself, a neuron is not intelligent, but when grouped
together, neurons are able to pass electrical signals through networks.
Fig-3.1: internal structure of a neuron.
Research has shown that a signal received by a neuron travels through the
dendrite region, and down the axon. Separating nerve cells is a gap called the synapse. In
15. order for the signal to be transferred to the next neuron, the signal must be converted
from electrical to chemical energy. The signal can then be received by the next neuron
and processed.
Warren McCulloch after completing medical school at Yale, along with Walter
Pitts a mathematician proposed a hypothesis to explain the fundamentals of how neural
networks made the brain work. Based on experiments with neurons, McCulloch and Pitts
showed that neurons might be considered devices for processing binary numbers. An
important back of mathematic logic, binary numbers (represented as 1's and 0's or true
and false) were also the basis of the electronic computer. This link is the basis of
computer-simulated neural networks, also know as parallel computing.
6
3.4 Top down approaches
A century earlier the true / false nature of binary numbers was theorized in 1854
by George Boole in his postulates concerning the Laws of Thought. Boole's principles
make up what is known as Boolean algebra, the collection of logic concerning AND, OR,
NOT operands.
Fig 3.2: Charts representing the logic of expert systems.
Using a similar set of rules, experts can have a variety of applications. With
improved interfacing,Because of the large storage capacity of computers, expert systems
had the potential to interpret statistics, in order to formulate rules. An expert system
works much like a detective solves a mystery. Using the information, and logic or rules,
16. an expert system can solve the problem. For example it the expert system was designed to
distinguish birds it may have the following as shown in Fig 3.
3.5 Artificial intelligence techniques in software engineering (aitse)
Software Engineering is a knowledge-intensive activity, requiring extensive
knowledge of the application domain and of the target software itself. Many Software
products costs can be attributed to the ineffectiveness of current techniques for managing
this knowledge, and Artificial Intelligence techniques can help alleviate this situation.
7
Fig- 3.3: Traditional software development process
The traditional view of software development process begins at the requirements
specification and ends at testing the software. At each of these stages, different kinds of
knowledge (design knowledge at design stage and programming and domain knowledge
17. at the coding stage) are required. At each of the two stages: design and coding, exist a
cycle: error recognition and error correction. Experience shows that errors can occur at
any stage of software development. Errors due to coding may occur because of faulty
design. Such errors are usually expensive to correct.
A basic problem of software engineering is the long delay between the
requirements specification and the delivery of a product. This long development cycle
causes requirements to change before product arrival. In addition, there is the problem of
phase independence of requirements, design and codes. Phase independence means that
any decision made at one level becomes fixed for the next level. Thus, the coding team is
forced to recode whenever there is change in design. Expert system use knowledge rather
than data to control the solution process. Knowledge engineers build systems by eliciting
knowledge from experts, coding, that knowledge in an appropriate form, validating the
knowledge, and ultimately constructing a system using a variety of building tools. The
main phases the expert system development processes are:-
• Planning
• Knowledge acquisition and analysis
• Knowledge design
• Code
8
• Knowledge verification
• System evaluation
3.6 Expert systems
A ``knowledge engineer'' interviews experts in a certain domain and tries to
embody their knowledge in a computer program for carrying out some task. How well
this works depends on whether the intellectual mechanisms required for the task are
within the present state of AI. When this turned out not to be so, there were many
disappointing results. One of the first expert systems was MYCIN in 1974, which
diagnosed bacterial infections of the blood and suggested treatments. It did better than
medical students or practicing doctors, provided its limitations were observed. Namely,
its ontology included bacteria, symptoms, and treatments and did not include patients,
doctors, hospitals, death, recovery, and events occurring in time. Its interactions
18. depended on a single patient being considered. Since the experts consulted by the
knowledge engineers knew about patients, doctors, death, recovery, etc., it is clear that
the knowledge engineers forced what the experts told them into a predetermined
framework. In the present state of AI, this has to be true. The usefulness of current expert
systems depends on their users having common sense.
Fig-3.4: Expert System development
3.7 Risk management:
The Risk Management process is a method of identifying risks in advance and
establishing methods of avoiding those risks and /or reducing the impact of those risks
should they occur. The process of risk management begins during the analysis phase of
software development life cycle. However, the actual process of managing risks
9
continues throughout the product development phase. The given Figure displays the steps
of the risk management process. Formally, articulated, risk management process consists
of three steps:.
19. Fig 3.5: Risk Management Process
Risk management strategies utilize lot of developer time and in software
development phases there is a link between all the phases by introducing a isolation phase
among the phases we can reduce the time in development by revisiting each phase after
changes in requirements. By using AI based systems with the help of automated tool or
automated programming tool we can eliminate risk assessment phase saving our time in
software development. Because of AITSE we can reduce the development time in
software development. Coding phase in software development process can be changed
into Genetic Code.
10
20. CHAPTER-4
FUNCTIONS OF ARTIFICIAL INTELLIGENCE
4.1 Game Playing:
Programming computers to play games such as chess and checkers .
4.2 Expert Systems:
Programming computers to make decisions in real-life situations (for example,
some expert systems help doctors diagnose diseases based on symptoms). A computer
application that performs a task that would otherwise be performed by a human
expert. For example, there are expert systems that can diagnose human illnesses,
make financial forecasts, and schedule routes for delivery vehicles. Some expert
systems are designed to take the place of human experts, while others are designed to
aid them.
Expert systems are part of a general category of computer applications known as
artificial intelligence . To design an expert system, one needs a knowledge engineer,
an individual who studies how human experts make decisions and translates the rules
into terms that a computer can understand.
4.3 Natural Language:
Programming computers to understand natural human languages. A human
language. For example, English, French, and Chinese are natural languages. Computer
languages, such as FORTRAN and C, are not. Probably the single most challenging
problem in computer science is to develop computers that can understand natural
languages. So far, the complete solution to this problem has proved elusive, although a
great deal of progress has been made. Fourth-generation languages are the programming
languages closest to natural languages.
21. 11
4.4 Neural Networks:
Systems that simulate intelligence by attempting to reproduce the types of
physical connections that occur in animal brains .The study of artificial neural networks
began in the decade before the field AI research was founded. In the 1960s Frank
Rosenblatt developed an important early version, the perceptron.[149] Paul Werbos
developed the back propagation algorithm for multilayer perceptrons in 1974, which led
to a renaissance in neural network research and connectionism in general in the middle
1980s. The Hopfield net, a form of attractor network, was first described by John
Hopfield in 1982.
Common network architectures which have been developed include the feed
forward neural network, the radial basis network, the Kohonen self-organizing map and
various recurrent neural networks.[citation needed] Neural networks are applied to the
problem of learning, using such techniques as Hebbian learning, competitive learning and
the relatively new architectures of Hierarchical Temporal Memory and Deep Belief
Networks.
4.5 Robotics:
Programming computers to see and hear and react to other sensory stimuli. The
field of computer science and engineering concerned with creating robots, devices that
can move and react to sensory input. Robotics is one branch of artificial intelligence.
Robots are now widely used in factories to perform high-precision jobs such as
welding and riveting. They are also used in special situations that would be dangerous for
humans for example, in cleaning toxic wastes or defusing bombs. Although great
advances have been made in the field of robotics during the last decade, robots are still
not very useful in everyday life, as they are too clumsy to perform ordinary household
chores. Robot was coined by Czech playwright Karl Capek in his play R.U.R (Rossum's
Universal Robots), which opened in Prague in 1921. Robota is the Czech word for forced
labor.
22. The term robotics was introduced by writer Isaac Asimov. In his science fiction
book I, Robot, published in 1950, he presented three laws of robotics:
12
1. A robot may not injure a human being, or, through inaction, allow a human
being to come to harm.
2. A robot must obey the orders given it by human beings except where such
orders would conflict with the First Law.
3. A robot must protect its own existence as long as such protection does not
conflict with the First or Second Law.
4.6 Fuzzy Logic
Fuzzy logic is a form of multi-valued logic derived from fuzzy set theory to deal
with reasoning that is approximate rather than precise. In binary sets with binary logic, in
contrast to fuzzy logic named also crisp logic, the variables may have a membership
value of only 0 or 1. Just as in fuzzy set theory with fuzzy logic the set membership
values can range (inclusively) between 0 and 1, in fuzzy logic the degree of truth of a
statement can range between 0 and 1 and is not constrained to the two truth values {true
(1), false (0)} as in classic predicate logic. And when linguistic variables are used, these
degrees may be managed by specific functions, as discussed below.
The term "fuzzy logic" emerged as a consequence of the development of the
theory of fuzzy sets by Lotfi Zadeh. A paper introducing the concept without using the
term was published by R.H. Wilkinson in 1963 and thus preceded fuzzy set theory.
Wilkinson was the first one to redefine and generalize the earlier multivalued logics in
terms of set theory. The main purpose of his paper, following his first proposals in his
1961 Electrical Engineering master thesis, was to show how any mathematical function
could be simulated using hardwired analog electronic circuits. He did this by first
creating various linear voltage ramps which were then selected in a "logic block" using
diodes and resistor circuits which implemented the maximum and minimum Fuzzy Logic
rules of the INCLUSIVE OR and the AND operations respectively. He called his logic
"analog logic".
23. 13
ADVANTAGES
1. It involves human like thinking.
2. They handle noisy or missing data.
3. They can work with large number of variables or parameters.
4. They provide general solutions with good predictive accuracy.
5. System has got property of continuous learning.
6. They deal with the non-linearity in the world in which we live.
24. 14
CONCLUSION
Technology is neither good nor bad. It never has been. What man does with it is
another story entirely. Technological changes are certainly coming. One of the most
challenging approaches facing experts is building systems that mimic the behavior of the
human brain, made up of billions of neurons. In the quest to create intelligent machines,
the field of Artificial Intelligence has split into several different approaches based on the
opinions about the most promising methods and theories neurons, while the top-down
approach attempts to mimic the brain's behavior with computer programs. The more use
we get out of the machines the less work is required by us. In turn less injuries and stress
to human beings. Human beings are a species that learn by trying, and we must be
prepared to give AI a chance seeing AI as a blessing, not an inhibition.
Artificial Intelligence technology is being used in many various field to achieve
accuracy, speed, reliability, etc,. various fields that are using Artificial intelligence are
given below.
• Driverless transportation.
• Surgical aid robots.
• Automated assembly lines and Dangerous jobs.
• Next generation traffic control.
