1. TECHNICAL PAPER PRESENTATION
ON
ARTIFICIAL INTELLIGENCE
&
EXPERT SYSTEMS
PRESENTED BY:
D.TEJA SREE.
12HP1A0455,
CH. DEEPIKA.
12G21AO422,
AUDISANKARA COLLEGE OF ENGG FOR WOMEN

2.  INTRODUCTION
 HISTORY
 OVERVIEW
 APPLICATIONS
 ADVANTAGES & DISADVANTAGES
 FUTURE
 CONCLUSION
 REFERENCES
ABSTRACT:
A.I. or Artificial Intelligence is an umbrella
term which spans over a whole array of
topics. These include computer science,
medicine, engineering and design just to
name a few. This versatility of A.I. comes
from the fact that Artificial Intelligence is
nothing less than an entity in itself,
capable of learning and

3. communicating,not just another computer
tool.
A.I. like many other world shaking
technologies had a small start and its
potential was limited because of the limit
on the computation power in 1956.
However since then the growth of A.I. has
been monumental. Basically being funded
by the same defense corporation that
funded the beginning of the internet
(DARPA), it has now managed to silently
enter almost all parts of a society.
The applications of A.I. today are far too
many to be listed. The most important and
interesting ones among them are definitely
the ones in computer application, such as
face recognition or handwriting
recognition systems, or even a world chess
champion program (that defeated Gary
Kasparov, the world chess
champion).However, like all technologies
A.I. has positive and negative sides to it
and most importantly it has a whole
variety of ethical considerations associated
with it that no technology has had any
trouble with previously.
This report covers topics including the
origin of A.I. and the steps taken towards
advancement since then, a brief
introduction of A.I. and its limitless
possibilities, the various branches and a
few of the numerous applications of A.I.,
the various advantages and
disadvantages that A.I. can provide as well
as what ethical dilemma Artificial
Intelligence poses to society with its
advancement.
INTRODUCTION
In 1941, the invention of electronic
computer and advancements in Computer
theory led to computer science and AI.
Nobert Wiener theorized that all
intelligent behavior was the result of
feedback mechanisms that could possibly
be simulated by machines. ‘Logic
Theorist’ is considered to be the first AI
program and John McCarthy as the
Father of AL, who developed the LISP
language.
The applications of AI range from
decision support system for process
control for PCB assembly, biologically
inspired intelligent robots using artificial
muscles, communication system design to

4. commerce and industry, military and
even for space exploration and even
pollution control.Some of the advantages
of AI are permanence, reproducibility,
efficiency, consistency, documentation,
completeness, timeliness, breadth of
knowledge.
But it has some fallacies too- like lack of
commonsense, creativity, learning,
sensory experience and degradation.
As for the future of AI, it could be
used to address problems that are
somewhat noisy and error resistant and
do not demand abstract reasoning and in
areas where people find jobs dangerous
and tedious.
Definition:Artificial Intelligence
(AI) is defined as intelligence
exhibited by an artificial (non-
natural, man-made) entity. The
modern definition of artificial
intelligence (or AI) is "the study and
design of intelligent agents" where
an intelligent agent is a system that
perceives its environment and takes
actions which maximizes its chances
of success. An Artificial Intelligent
System is also called a "Cognitive
Architecture"(or)"Adaptive Autonomous
Agent".
AI have many objectives, the major of
them are:
 Make machines smarter
 Understand what intelligence
is?
 Make machines more useful
AI is a combination of computer
science, physiology, and philosophy.It is a
broad topic, consisting of different fields,
from machine vision to expert systems.
The element that the fields of AI have in
common is the creation of machines that
can "think".
In order to classify
machines as "thinking", it is necessary to
define intelligence. To what degree does
intelligence consist of, for example,
solving complex problems, or making
generalizations and relationships? And
what about perception and
comprehension? Research into the areas
of learning, of language, and of sensory
perception has aided scientists in building
intelligent machines. One of the most
challenging approaches facing experts is
building systems that mimic the behavior
of the human brain, made up of billions of

5. neurons, and arguably the most complex
matter in the universe. Perhaps the best
way to gauge the intelligence of a machine
is British computer scientist Alan
Turing's test. He stated that a computer
would deserve to be called intelligent if it
could deceive a human into believing that
it was human.
.
HISTORY
Timeline of major AI events
Evidence of Artificial Intelligence folklore
can be traced back to ancient Egypt, but
with the development of the electronic
computer in 1941, the technology finally
became available to create machine
intelligence. Era of the Computer: In
1941 the invention of electronic computer
revolutionized every aspect of the storage
and processing of information. The 1949
innovation, the stored program computer,
made the job of entering a program
easier, and advancements in computer
theory lead to computer science, and
eventually Artificial intelligence
The Beginnings of AI :
It was not until the early 1950's
that the link between human intelligence and
machines was really observed. Norbert Wiener
was one of the first Americans to make

6. observations on the principle of feedback
theory feedback theory, example the
thermostat. He theorized that all intelligent
behavior was the result of feedback
mechanisms that could possibly be simulated
by machines.
In late 1955, Newell and Simon
developed The Logic Theorist, considered
by many to be the first AI program. The
program, representing each problem as a
tree model, would attempt to solve it by
selecting the branch that would most
likely result in the correct conclusion.In
1956 John McCarthy regarded as the
father ofAI, organized a conference in
Vermont for "The Dartmouth summer
research project on artificial
intelligence." From that point on, because
of McCarthy, the field would be known as
Artificial intelligence it
brought together the founders in
AI, and served to lay the groundwork for
the future of AI research.
Knowledge Expansion
Centers for AI research began
forming at Carnegie Mellon and MIT,
and a new challenges were faced: further
research was placed upon creating
systems that could efficiently solve
problems, by limiting the search, such as
the Logic Theorist . And second , making
systems that could learn by themselves.
In 1957, the first version of a
new program The General Problem
Solver (GPS) was tested .
In 1958 McCarthy announced his
new development; the LISP language,
which is still used today. LISP stands for
LISt Processing, and was soon adopted as
the language of choice among most AI
developers.

7. In 1963 MIT received a 2.2 million
dollar grant from the United States
government to be used in researching
Machine-Aided Cognition (artificial
intelligence). The grant by the
Department of Defense's Advanced
research projects Agency (ARPA), to
ensure that the US would stay ahead of
the Soviet Union in technological
advancements
The Multitude of programs
One notably was SHRDLU which
was part of the microworlds project,
which consisted of research and
programming in small worlds (such as
with a limited number of geometric
shapes, by Marvin Minsky).Another
advancement in the 1970's was the advent
of the expert system. Expert systems
predict the probability of a solution under
set conditions, which had the potential to
interpret statistics, to formulate rules.
With analysis of this information, frames
of what an image might be could then be
referenced. another development during
this time was the PROLOGUE language
in 1972,During the 1980's Expert systems
in particular demand because of their
efficiency. Companies such as Digital
Electronics were using XCON, an expert
system designed to program the large
VAX computers. DuPont, General
Motors, and Boeing relied heavily on
expert systems.
The Transition from Lab to Life:
The impact of the computer technology,
AI included was felt. No longer was the
computer technology just part of a select
few researchers in laboratories. The
personal computer made its debut along
with many technological magazines.
AI put to the Test

8. The military put AI based
hardware to the test of war during Desert
Storm. AI-based technologies were used
in missile systems, heads-up-displays, and
other advancements. AI has also made the
transition to the home. Applications for
the Apple Macintosh and IBM compatible
computer, such as voice and character
recognition have become available.
OVERVIEW: ARTIFICIAL
INTELLIGENT SYSTEMS
Artificial Intelligence Programs:
Computer scientists have written
programs that are complete intelligent
systems. They have an input
corresponding to senses, a choice of
actions based on response rules,
sometimes called "productions", and
the ability to act, be it as graphics on a
computer screen, as a text output or as
limb movements. Most have a memory
for storing experiences and the ability to
learn.
Neural Nets:
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. By itself,
a neuron is not intelligent, but when
grouped together, neurons are able to
pass electrical signals through networks
.

9. The neuron "firing", passing a signal to the next in the chain.
Autonomous Humanoid Robots
It is not enough to just write a computer
program that is an intelligent system and
runs within a computer with an output on
a screen. We need to build complete
systems that act in our human
environment.
How a robot brain works:
The senses of the robot report
information to the brain. Here the brain
checks if it has a concept for the
information that it receives. If not, it
creates a composite concept that has as
parts the various sense informations. A
different area of the brain checks in the
memory, It selects one of the appropriate
response rules and sends the action part,
also a concept, to the
limbs which then do
the action.At the
start of the robots "life" the memory is
empty. But every time the robot has an
experience it stores a new response rule
APPLICATIONS
AI with its learning capabilities can
accomplish tasks that require detailed
instructions followed and mental
alertness but only if the worlds
conservatives are ready to change and
allow this to be a possibility.There are so
many things that can go wrong with a
new system so we must be as prepared as
we can be for this new technology.
Something as revolutionary as AI is sure
to have many kinks to work out
• Decision support system for process control in electronics
assembly:

10. The major objective of this system
is to provide decision support for
process control in PCB assembly.
The system will be expected to
have the abilities to accommodate
environmental and process
variability and to learn from past
successful experience.
• An Intelligent Modeling System:
Constraint satisfaction problems
have been extensively studied by
researchers in both the operations
research (OR) and artificial
intelligence (AI) areas. The
research aimed at integrating the
two approaches so that some of
their limitations can be removed is
described. Specifically, a
knowledge-based system that
formulates and maintains OR
models for manufacturing
planning purposes is presented.
• Biologically Inspired Intelligent Robots Using Artificial
Muscles:
Technology evolution led to such
fields as artificial muscles,
artificial intelligence, artificial
vision and biomimetic capabilities
in materials science, mechanics,
electronics, computing science,
information technology and many
others. One of the newest fields is
the artificial muscles, which is the
moniker for electroactive polymers
(EAP).
• Communication System Design:
A knowledge-based design support
system, called KDSS will help
designers inexperienced in
communication system design easily
create advanced systems like
intelligent networks, and large-scale
distributed computing.

11. • Environmental pollution and the conservation:
The recent advancements of AI-
based technologies for
management and control of
pollution minimization and
mitigation processes are examined.
Several demanding areas for
enhanced research efforts are
discussed, including issues of data
availability and reliability,
methodology validity, and system
complexity.
• In commerce and industry:
AI & ES is used for such varied
purposes like case-based
reasoning, genetic algos, planning
and workflow, in building
knowledge based systems.
• Human Space Exploration:
AIS which use minimally specified
descriptions or models interacting
and updating information (e.g.,
sensor information) to perform
required functions inspace
exploration-related areas:
(1) Intelligent systems for process
control functions, for automated
diagnosis and repair functions, for
data monitoring, and for status
reporting; and
(2) System architectures for the
integration of potentially complex
interactions between and within
intelligent system components, and
interactions between systems and
the environments in which they
function. Also of interest are
approaches to engineering design
and design knowledge capture for
intelligent systems, tools to aid
crew and ground support in
updating intelligent system
software, innovative approaches to
lower level controlling software
and hardware in support of
intelligent systems, and
approaches to increasing the
reliability of space exploration

12. systems through the application of
intelligent system principles.
• Military Applications:
Artificial intelligence
methodologies are being applied to
support decision making at all
levels of military operations in
time-sensitive environments.
Applications being studied include
assessing force readiness,
reliability and capability; planning
complex missions; and integrating
data from multiple sources.
• Intelligent Web Sites for E-Business:
Decision Script is a Web
application server and
development platform for building
server-side, JavaScript
applications that use artificial
intelligence. It is the first tool on
the market developed explicitly for
Web designers who build sites with
decision-making capabilities.
Unlike static web sites, it interacts
with each visitor during his/her
own session on a server by
dynamically constructing
individualized Web pages in
response to the information the
visitor supplies.
ADVANTAGES
• Permanence - Expert systems do
not forget, but human experts may
• Reproducibility - Many copies
of an expert system can be made,
but training new human experts is
time-consuming and expensive .If
there is a maze of rules (e.g. tax
and auditing), then the expert
system can "unravel" the maze
• Efficiency - can increase
throughput and decrease
personnel costs
o Although expert systems
are expensive to build and
maintain, they are
inexpensive to operate

13. o Development and
maintenance costs can be
spread over many users
o The overall cost can be
quite reasonable when
compared to expensive and
scarce human experts
o Cost savings:
Wages - (elimination of a
room full of clerks)
Other costs - (minimize
loan loss)
• Consistency - With expert
systems similar transactions
handled in the same way. The
system will make comparable
recommendations for like
situations.
Humans are influenced by
o recency effects (most recent
information having a
disproportionate impact on
judgment)
o primacy effects (early
information dominates the
judgment).
• Documentation - An expert
system can provide permanent
documentation of the decision
process
• Completeness - An expert
system can review all the
transactions, a human expert can
only review a sample
• Timeliness - Fraud and/or errors
can be prevented. Information is
available sooner for decision
making
• Breadth - The knowledge of
multiple human experts can be
combined to give a system more
breadth that a single person is
likely to achieve
• Reduce risk of doing business
o Consistency of decision
making
o Documentation
o Achieve Expertise
• Entry barriers - Expert systems
can help a firm create entry
barriers for potential competitors
• Differentiation - In some cases,
an expert system can differentiate
a product or can be related to the
focus of the firm (XCON)
• Computer programs are best in
those situations where there is a
structure that is noted as
previously existing or can be
elicited

14. DISADVANTAGES
• Common sense - In addition to a
great deal of technical knowledge,
human experts have common
sense. It is not yet known how to
give expert systems common sense.
• Creativity - Human experts can
respond creatively to unusual
situations, expert systems cannot.
• Learning - Human experts
automatically adapt to changing
environments; expert systems
must be explicitly updated. Case-
based reasoning and neural
networks are methods that can
incorporate learning.
• Sensory Experience - Human
experts have available to them a
wide range of sensory experience;
expert systems are currently
dependent on symbolic input.
• Degradation - Expert systems
are not good at recognizing when
no answer exists or when the
problem is outside their area of
expertise.
FUTURE:
The future holds a couple of likely
possibilities. The first is that AI develops
into a field dominated by simple, adaptive
systems; like those to intelligently route
telephone communications, or optimize
certain processes. There may even be
intelligent text retrieval systems which
search using keywords and so forth which
is believed to be the most economically
advantageous and realistic.
A second possibility is the popularized
idea of omnipotence, like that potentially
embodied by the CYC project. The
rationale is the old idea (probably due to
Minsky) that once an appropriate
threshold is reached, an AI system will be
able to begin controlling its own learning
and evolution .This position is normally
characterised by the belief in
"disembodied" intelligence - intelligence
restricted to the symbol processing
domain. If this becomes a reality, it seems
clear the *kind* of intelligence embodied
in this approach may be fundamentally
alien to our own. Of course there is room
here for in-betweens.
Finally, a quote from Alan
Turing.”Considering the degree of
obfuscation which seems to plague AI
work today, I believe Turing's writings
provide a strikingly clear, common sense
understanding of intelligent machines. "It
has been said that computing machines
can only carry out the processes that they

15. are instructed to do. But is it necessary
that they should always be used in this
manner? Let us suppose we have set up a
machine with certain initial instruction
tables, so constructed that these tables
might on occassion, if good reason arose,
modify these tables. One can imagine that
after the machine had been operating for
some time, the instructions would have
altered out of all recognition, but never
theless still be such that one would have
to admit that the machine was still doing
very worthwhile calculations. Possibly it
might still be getting results of the type
desired when the machine was first set
up, but in a much more efficient manner.
When this happens I feel that one is
obliged to regard the machine as showing
intelligence. As soon as one can provide a
reasonably large memory capacity it
should be possible to begin experiment on
these lines." -Alan TuringSo what will
happen over the next thirty years is that
will see new types of animal-inspired
machines which will change over time as
a result of their interactions with us and
with the world. These silent, pre-
linguistic, animal-like machines will be
nothing like humans but they will
gradually come to seem like a strange sort
of animal will finally become mainstream
and enter the public consciousness. These
machines could address problems that are
somewhat noise and error resistant and
that do not demand abstract
reasoning.And robots that can learn to
walk can learn other sensorimotor skills
that we can neither articulate nor
perform ourselves. These are all sorts of
other uses for artificial animals in areas
where people find jobs dangerous or
tedious - land-mine clearance, toxic waste
clearance, farming, mining, demolition,
finding objects and robotic exploration,
for example.
CONCLUSION
Although some people feel science
has discovered most of what's knowable,
it can easily be said that we are still pretty
much at the very beginning. To praphrase
an old joke: the ratio of what we know
now to what we knew a hundred years
ago is truly enormous; but the difference
is minute! You can't expect to build single
isolated AI's, alone in laboratories, and
get anywhere. Unless the creatures can
have the space in which to evolve a rich
culture, with repeated social interaction
with things that are like them, you can't
really expect to get beyond a certain
stage. If we work up from insects to dogs
to Homo erectus to humans, the AI
project will I claim fall apart somewhere
around the Homo erectus stage because of

16. our inability to provide them with a real
cultural environment.
The time has come to develop the True Artificial Intelligence System
1.Hardware power of modern
computers is huge. Modern programs
don’t use all computers’ hardware
resources fully. Computers idle more than
95% of the time – programmers simply
don’t know how to efficiently use
computers’ hardware capabilities.
REFERENCES
 www.google.com
 www.howstuffworks.com
 www.hotbot.com
 Electronics for You
 Science Reporter
 IEEE Journal on Transactions on AI