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Abstract

The recent growth in computer architecture has changed the face of science and engineering. This growth is so fundamental that it is dramatically reshaping relationships among people and organizations and providing a foundation for understanding and learning of intelligent behavior in living and engineered systems. Is this growth beneficial to our society and how the medical facilities, police departments, and manufacturing plants have all been changed by this, are such questions of the general public which are due to the lack of education concerning rapidly advancing technologies. This paper attempts to present an overview of Artificial Intelligence (AI). A generally accepted theory that "machine will do and think like humans more in the future" is the concept behind AI. Brief literature of different aspects by which AI is achieved like expert system, knowledge based systems (knowledge engineering), neural networks, fuzzy logic, neuro fuzzy logic and fuzzy expert system, is included in order to have a clear understanding of AI. Along with the different applications of AI, application of expert system to solve the design problem of mechanical spring is also included in this paper. It is concluded that extensive ongoing research in the field of AI gives an idea that in near future a day will come when human beings and machines will merge into cyborgs or cybernetic organisms that are more capable and powerful than either. This idea is called transhumanism
SBIT JOURNAL OF SCIENCES AND TECHNOLOGY ISSN 2277-8764
VOL-2, ISSUE 1, 2013.
AN OVERVIEW OF ARTIFICIAL INTELLIGENCE
Gyanendra Singh 1 Ajitanshu Mishra2 Dheeraj Sagar3
1,2,3Assistant Professor, Mechanical Engineering Department,
Invertis University Bareilly
E-mail:gyanendra.rkgit@gmail.com
Abstract: The recent growth in computer architecture has changed the face of science and engineering. This growth is
so
fundamental that it is dramatically reshaping relationships among people and organizations and providing a
foundation for understanding and learning of intelligent behavior in living and engineered systems. Is this growth
beneficial to our society and how the medical facilities, police depar tments, and manufacturing plants have all been
changed by this, are such questions of the general public which are due to the lack of education concerning rapidly
advancing technologies.
This paper attempts to present an overview of Artificial Intelligence (AI). A generally accepted theory that
“machine will do and think like humans more in the future” is the concept behind AI. Brief literature of different
aspects by which AI is achieved like expert system, knowledge based systems (knowledge engineering), neural networks,
fuzzy logic, neuro fuzzy logic and fuzzy expert system, is included in order to have a clear understanding of AI. Along with
the different applications of AI, application of expert system to solve the design problem of mechanical spring is also
included in this paper.
It is concluded that extensive ongoing research in the field of AI gives an idea that in near future a day will
come when human beings and machines will merge into cyborgs or cybernetic organisms that are more capable and
powerful than either. This idea is called transhumanism
Key words: Artificial Intelligence, Expert System, Neural Network, Fuzzy Logic
1. INTRODUCTION
Artificial Intelligence term was coined by
John McCarthy in 1956. He defined it as "the science
and engineering of making intelligent machines.” AI is
the branch of computer science which deals with the
study and design of intelligent agents that perceives its
environment and takes actions which maximize its
chances of success. AI may be defined as: “The ability
to hold two different ideas in mind at the same time
and still remain the ability to fucntion”. But AI must
include the learning from past experience, reasoning
for the decision making, inference power and quick
response. Also it must be able to take decisions on the
basis of priorities and tackle complexity and ambiguity.
Machines programmed to carry out tasks, when carried
out by humans would require intelligence, are said to
possess artificial intelligence. AI's scientific goal is to
understand intelligence by building computer programs
that exhibit intelligent behavior by using symbolic
inference, or reasoning inide the machine. AI definition
is not time-independent. It gives the judgment of any
system by keeping time in mind.
2. FEATURES OF AI PROGRAMMING
According to Brook ordinary programming
languages don’t have the abilities to deal with
qualitative information. So, the AI machines, are
programmed to work with their own developed
programming language to manipulate knowledge more
effectively. AI programs are different from ordinary
programming languages. They are written to
manipulate predominantly qualitative rather than
numeric information. They use declarative knowledge,
i.e. assertions whose truth-value is independent of the
algorithmic context. They can induce, deduct and
sometimes guess data. They can reconsider decisions
by employing back tracking for solutions.
3. COMPONENTS OF AI
AI has four main components
1. Expert systems:
2. Heuristic problem solving
3. Natural Language Processing
4. Vision
Expert system handles the situation as an expert and
gives performance. Heuristic problem solving is meant
to evaluate small range of solutions, may involve some
guesswork to find near optimal solution. Natural
language processing provides communication between
human and machine in natural language. Vision is the
ability to recognize shapes and features etc.
automatically.
4. EXPERT SYSTEM
An expert system is a machine system in
which useful human knowledge is added in machine
memory in order to give intelligent advice and offer
explanations and justifications of its decisions or
demand. Expert systems relies on a large database of
well defined specialized knowledge about a particular
area. Construction of such programs is refered to as
Knowledge Engineering. All such AI programs that
achieve expert-level competence in solving problems
in task areas by using knowledge about specific tasks
are called knowledge-based Systems or expert systems.
These programs contains the knowledge used by
1
human experts, in contrast to knowledge gathered from
textbooks. Because of this expert systems are like
human experts e.g. doctors, engineers, analysts,
teachers, geologists etc which encapsulate the skills of
an expert and to dispense advice to less knowledgeable
users. This transfer of knowledge depends upon the
task and will take place gradually through many
interactions between expert and the system.
It is easier to build expert system than ones
with common sense. They represent task domain. Task
means some goal-oriented, problem-solving activity
and domain refers to the area within which the task is
being performed. One of the earliest expert system
MACSYMA which performed a variety of symbolic
mathematical tasks, was composed of a set of fairly
unstructured LISP functions. There are many expert
systems exists which have been designed for giving
expertise training, designing and trouble-shooting etc.
like MYCIN, TURNX, PROSPECTOR. The expert
systems are still in their infancy.
4.1 Benefits of Expert Systems
Following are the benefits of expert system
a) Expert systems have proved to do a better job
than humans. They make fewer mistakes and
more consistent in their recommendations.
b)
Artificial Expertise is usually cheaper
compared to human expertise.
c) They achieved a notable success in the field of
training, to train non-experts and even to
improve expertise of expert.
d)
They can handle the mechanical type of
repetitive tasks of experts, so that experts can
well concentrate on their unique skills in
given domain.
e) They are compatible with many manager’s
decision styles.
f) They can enable operations in environment
not suitable for humans.
g) They improve the productivity of industry.
4.2 Expert System Applied to Design of Spring
An expert systems are able to make flexible
programs by using qualitative and quantitative data to
solve a wide range of design problems. An expert
system can be used to design the spring. It requires
minimum input from user like one load and length of
spring. Any additional information like second load at
different length of spring rate, spring inner diameter
and outer diameter limits can also be specified if
required because this additional information reduces
the guesswork of expert system. Knowledge Base,
Rule Base and Data Base are used by the user while
designing the spring.
a) The Knowledge Base consists of following two
equations:
K= Gd4/ (8D3N) and T= 8Ks FD/ (πd3),
Here F represents load on the spring, T represents shear
stress, K s represents shear stress concentration factor,
D represents mean coil data, D represents wire
diameter, G represents shear modulus and N represents
number of active coils.
2
b) Rule Base consists of
constraints like length, size
limits on outer diameter, inner diameter, cost of
material, etc which are provided by user. Inference
engine is used to test the design under these
constraints.
c) Data Base database consists of around
700
compression designs. When the design completes, the
inference engine compares this design with these
standard compression designs to find the optimal
solution. The results are reported to user.
d) User Interface is there throughout the designing
process. The expert system asks the user for any
additional input like any space limitations, any material
specification, any load requirements, any solid height
limitations etc.
After the completion of process by expert
system final selection is left to the user on the basis of
its application.
5. FUZZY LOGIC
It was introduced by Dr. Lotfi Zadeh of
UC/Berkeley known as father of fuzzy set theory, in
the 1960's as a means to model the uncertainty of
natural language. Lofti A.Zadeh. Fuzzy Logic are used
as a profitable tool to control the subway systems,
complex industrial processes, entertainment
electronics, diagnosis systems and household
appliances e.g. in washing machines fuzzy logic sense
load size and detergent concentration and adjust their
wash cycles automatically. Fuzzy logic is very useful
in manufacturing processes as it can handle situations
that can not be adequately handled by traditional
true/false logic. Fuzzy Logic was invented in the
United States and rapid growth of this technology has
started from Japan.
Fuzzy logic is a mathematical approach to
problem solving.
It is very powerful method of
reasoning when there is no simple mathematical model
and input data are imprecise for very complex
processes and highly nonlinear processes. It produces
exact results from imprecise data, and is especially
useful in computers and electronic applications. Fuzzy
logic differs from classical logic in that statements are
no longer true or false, on or off. In traditional logic an
object takes on a value of either zero or one but in
fuzzy logic, an object can assume any real value
between 0 and 1. The human brain can reason with
uncertainties and judgments. Computers can only
manipulate precise valuations. Fuzzy logic is an
attempt to combine the two techniques." Fuzzy logic
performs better when compared to conventional control
mechanism like PID.
According to Nagrath and Gopal Fuzzy Logic
is only a small part of the logics available to AI. Fuzzy
Logic is basically a multivalued logic derived from
fuzzy set theory to deal with reasoning that is
approximate rather than precise. Intermediate values
between conventional evaluations like yes/no,
true/false, black/white, etc. can be formulated
mathematically and processed by computers. In this
way an attempt is made to apply a more human-like
way of thinking in the programming of computers.
5.1 Fuzzy Expert System
A fuzzy expert system uses fuzzy logic
instead of Boolean logic. In other words, a fuzzy expert
system is a collection of membership functions and
rules that are used to reason about data. Fuzzy expert
systems are oriented toward numerical processing
whereas conventional expert systems are mainly
symbolic reasoning engines. The rules in a fuzzy
expert system are usually of a form similar to the
following:
if x is low and y is high then z = medium
where x and y are input variables (names for know data
values), z is an output variable (a name for a data value
to be computed), low is a membership function (fuzzy
subset) defined on x, high is a membership function
defined on y, and medium is a membership function
defined on z. The part of the rule between the "if" and
"then" is the rule's _premise_ or _antecedent_. The part
of the rule following the "then" is the rule's
_conclusion_ or _consequent_. This part of the rule
assigns a membership function to each of one or more
output variables. Fuzzy expert systems can have more
than one conclusion per rule and can also have more
than one rule. The group of rules is collectively known
as knowledge base. In a fuzzy expert system, the
inference process is a combination of four
subprocesses: fuzzification, inference, composition,
and defuzzification. The defuzzification subprocess is
optional. In fuzzification subprocess, the membership
functions defined on the input variables are applied to
their actual values, to determine the degree of truth for
each rule premise. In the inference subprocess, the
truth value for the premise of each rule is computed,
and applied to the conclusion part of each rule. This
results in one fuzzy subset to be assigned to each
output variable for each rule. In the composition sub
process, all of the fuzzy subsets assigned to each output
variable are combined together to form a single fuzzy
subset for each output variable. In defuzzification
subprocess fuzzy value is converted to a single number
crisp value.
5.2 Neuro-fuzzy Logic
They use combination of fuzzy logic and
neural networks. Fuzzy logic uses approximate human
reasoning in knowledge-based systems while the neural
networks aim at pattern recognition, optimization and
decision making. According to Arbib the explicit
knowledge representation of fuzzy logic is augmented
by the learning power of simulated neural networks.
This combination of two technological innovations
delivers the best results and has led to a new science
called neuro-fuzzy logic.
6. ARTIFICIAL NEURAL NETWORKS (ANN)
The concept of a neural network appears to
have first been proposed by Alan Turing in his 1948
paper "Intelligent Machinery". Artificial neurons were
3
first proposed in 1943 by Warren Mc Culloch, a
neurophysiologist, and Walter Pitts, an MIT logician.
Computers can be operated in nanoseconds,
and work without error. But it can’t do walking, talking
and reasoning like human being. ANN are an attempt
to emulate (very roughly) the basic functions of the
human brain to perform complex functions that
everyday computer systems are incapable of doing.
The human brain is a naturally occurring model of
Neural network. So, idea is to simulate functioning of
the brain directly on a computer and thus develop
artificial neurons. Neural Networks are not meant to
duplicate the human brain, but to receive information
about how the brain works.
An ANN involves a network of simple processing
elements (artificial neurons) which can exhibit complex
global behavior, determined by the connections between the
processing elements and element parameters whereas
Biological neural networks are made up of real
biological neurons that are connected or functionally
related in the peripheral nervous system or the central
nervous system.
In a neural network model a large number of very
simple neurons like nodes of processing elements are
connected together with a large number of weighted
connections between these elements which are highly
parallel and distributed. Neurons are extremely slow,
operate in milliseconds and yet humans can perform
extremely complex tasks in just a tenth of a second
because brain contains a huge number of processing
elements that act in parallel. According to Brook ANN
are used to solve artificial intelligence problems by
using algorithms designed to alter the strength of the
connections in the network to produce a desired signal
flow.
ANN can recognize something it has never seen
before and predict the future, by extracting patterns in
the past. Application areas of ANN also include the
system identification and control (vehicle control,
process control), function approximation or regrassion
analysis, time series prediction, modeling game-
playing, sequential decision making (chess, racing),
pattern recognition (radar systems, face identification,
object recognition, etc.), sequence recognition (gesture,
speech, handwritten text recognition), medical
diagnosis, financial applications, data processing
(including filtering, clustering), knowledge discovery
in databases (KDD), visualization and e-mail spam
filtering.
7. APPLICATION OF AI
a)
To design and analyze the mechanical
elements on basis of size limitations.
b) To diagnose electronics locomotion systems.
c)
Can be
used in electronics
and
electrochemical systems.
d)
To diagnose the software development
process.
e) To identify chemical compound structures and
chemical compounds.
f) Can be used in medical diagnosis.
g) To plan experiments in biology, chemistry and
molecular genetics.
h)
To make stock and bond portfolio for
selection and management.
i) Trouble shooting systems
j) To plan and explore the space.
k) To forecast crop damage
l) To develop completely automated plants and
industries.
8. CONCLUSION
Till now AI has not such a great effect directly
on common people life and is limited to some areas
like military, space, industry, medical, neutral networks
and geological.
It may be expected that at the end of 2035
with the extensive research and advancement in the
field of AI, we will be able to move away from today’s
machinery that necessarily come with weighty manuals
regarding machine languages and develop the
machinery which will be able to understand human
completely. We will have robot as doctor in hospitals,
professor in class room, driver in bus. According to
4
Bostrom that will be the era of transhumanism where
human beings and machines will merge into cyborgs or
cybernetic organisms that are more capable and
powerful than either.
9. REFERENCES
Arbib, Michael A. (Ed.) (1995), “The Handbook of
Brain Theory and Neural Networks”, p. 666
Bostrom, Nick (2005) "A history of transhumanist
thought" , Journal of Evolution and Technology, p.
2-21
Brook R.A.
Intelligence without representation-
Artificial Intelligence” 1991,p.139-159
Nagrath I.D. and Gopal M.
Control Systems
Engineering” New Age International Publications.
PC "Fuzzy Logic and Neural Networks - Practical
Tools for Process Management", May/June, 1994,
p.17
Surinder Kumar and Jha A.K. CAD/CAM

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The Handbook of Brain Theory and Neural Networks”, p. 666 Bostrom, Nick (2005) A history of transhumanist thought
  • Arbib
  • A Michael
Arbib, Michael A. (Ed.) (1995), “The Handbook of Brain Theory and Neural Networks”, p. 666 Bostrom, Nick (2005) A history of transhumanist thought , Journal of Evolution and Technology, p
Control Systems Engineering New Age International Publications. PC "Fuzzy Logic and Neural Networks -Practical Tools for Process Management
  • I D Nagrath
  • M Gopal
Nagrath I.D. and Gopal M. " Control Systems Engineering " New Age International Publications. PC "Fuzzy Logic and Neural Networks -Practical Tools for Process Management", May/June, 1994, p.17