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THE POSITIVE IMPACT OF ROBOTICS, ARTIFICIAL INTELLIGENCE (AI) AND EXPERT SYSTEMS IN THE 21ST CENTURY
A TERM PAPER
WRITTEN BY: ZAKARIYA, N. I. REG.: 00-GM/ICT/00566/PE
DEPT. OF INFORMATION AND COMPUTER TECHNOLOGY
FEDERAL UNIVERSITY OF TECHNOLOGY OWERRI (FUTO), PORTHARCOURT EXTENSION
AUGUST 2001 ©
CHAPTER ONE
INTRODUCTION
1.0 DEFINITIONS
Lets start by defining the following key words;
Ø Robotics
Ø Artificial Intelligence
Ø Expert system
1.1 ROBOTICS
A robot is a programmable multifunction device designed to move material,
parts, tools or specialized devices through variable programmed motions
for the performance of variety of tasks.
The term robot conjure up a vision of a mechanical man – that is, some,
android as viewed in star wars or other science fiction movies. The
industrial robot are largely unstrained and defined by what we have so far
managed to do with them.
In the last decade, the industrial robot (IR) has developed from concept to
reality and robots are now used in factories throughout the world. In lay
terms, the industrial robot would be called a mechanical arm. This
definition, however, includes almost all factory devices that have a moving
lever.
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It’s generally agreed that the three main components of robot are the
mechanical manipulator, the actuation mechanism and the controller.
1.1.1 MECHANICAL MANIPULATOR
The mechanical manipulator of an industrial robot (IR) is made up of a set
of axes (either rotatary or slide), Typically three to six per IR. The first three
axes determine the work envelop of the IR. While the last three deals with
the wrist of the IR and the ability to orient the hand. Many robots are more
restricted in their motions than the six-axis robot. Conversely, robots are
sometimes mounted on extra axes such as an X-Y table or track to provide
additional one or two axes.
It’s important to note at this point that the “hand” of the robot, which is
typically a gripper or tool. Specifically designed for one or two application is
not a part of a general purpose IR. Hands or end effectors, are special
purpose devices attached to the wrist of an IR.
1.1.2 ACTUATION MECHANISM
The actuation mechanism of an IR is typically hydraulic, pneumatic or
electric. More importance distinctions in capability are based on the ability
to employ servomechanism, which use feedback control to correct
mechanical position, as opposed to non-servo open-loop actuation systems.
Surprisingly non-servo open loop industrial robots perform many seemingly
complex tasks in today’s factories.
1.1.3 CONTROLLER
The controller is the device that stores the IR program and by
communication with the actuation mechanism controls the IR motions. IR
controllers have undergone the most evolution as IR’s have been
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introduced to the factory floor. The evolution has been in the method of
programming (human interface) and in the complexity of the programs
allowed.
1.2 SOCIAL IMPACT OF ROBOTICS ON THE PRODUCTION
LINE
What is robotics on the production line? Robotics on the production line is
the machines, which have been designed to manufacture products in
factories. An example of this would be machines, which are used to join car
parts in massive production. Prior to these machines the jobs would have
been carried out by paid workers. This means that machinery used on the
production line replaces many jobs, which would normally be carried out by
paid labor workers.
1.2.1 THE SOCIAL IMPACT.
There are many social impact as a result of robotics on the production line.
There are both positive and negative impacts. The social impacts include;
t Many previous employees loose their jobs as a result of being replaced
by mechanics, creating an increase in social unemployment.
t Cheaper for companies to use robotics rather than employing workers.
t Cheaper to consumers because companies can produce in mass
amounts and in a lot less time.
t Robotics produced products are generally higher in quality than other
products.
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CHAPTER TWO
ARTIFICIAL INTELLIGENCE
2.0 What is Artificial Intelligence (AI)?
AI is a branch of computer science concerned with the study and creation
of computer systems that exhibit some form of intelligence. System that
can learn new concepts and tasks, system that can reason and draw useful
conclusions about the world around us, system that can understand a
natural language or perceive and comprehend a visual scene and systems
that perform other types of feats that require human type of intelligence.
In short form, we can define AI in two ways. The first definition defines the
field and the second describes some of its functions.
1. Artificial Intelligence Research. This is the part of computer science that
is concerned with the symbol manipulation processes that produces the
intelligent action. By intelligent action is meant an act of decision that is
goal oriented, arrived at by an understandable chain of symbolic
analysis and reasoning steps and is one in which knowledge of the world
inform and guide the reasoning.
2. Artificial intelligent is a set of advanced computer software applicable to
classes of non-deterministic problems such as natural language
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understanding, image understanding, expert systems, knowledge
acquisition and representation, heuristic search, deductive reasoning
and planning.
Fundamental issues in artificial intelligence that must be resolved
· Representing the knowledge needed to act intelligently
· Acquiring knowledge and explaining it effectively
· Reasoning, drawing conclusions, making inferences and making
decisions
· Evaluating and choosing among alternatives.
An understanding of AI requires an understanding of related terms such as
intelligence, knowledge, reasoning, thought, cognition, learning and a
number of computer related terms.
While we lack precise scientific definitions for many of these terms, we can
give general definitions of them. And of course, one of the objectives of this
text is impact social meaning to all the terms related to AI, including their
operational meanings.
Dictionaries define intelligence as the ability to acquire, understand and
supply knowledge or the ability to exercise thought and reasons. Of course,
intelligence is more than this, it embodies all of the knowledge and feats
both conscious and unconscious which we have acquired through study and
experience; highly refined sight and sound perception, thought;
imagination; the ability to converse, read, write, drive a car, memorize and
recall facts, express and feel emotions and much more.
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Intelligence is the integrated sum of these facts, which gives us the ability
to remember a face not seen for thirty or more years or to build and send
rockets to the moon. It’s those capabilities, which set Homo sapiens apart
from other forms of living things. And as we shall see, for intelligence is
knowledge.
Can we ever expect to build systems, which exhibit these
characteristics? The answer to this is yes! Systems have already been
developed to perform many types of intelligent tasks and expectations are
high for near term development of even more impressive systems. We now
have systems, which can learn from examples, from being told from past
related experiences and through reasoning. We have systems, which can
solve complex problems in mathematics, in scheduling many diverse tasks,
in finding optimal system configurations, in planning complex strategies for
the military and for business, in diagnosing medical diseases and other
complex systems, to name a few. We have systems, which can understand
large parts of natural language. We have systems, which can see well
enough to recognize objects from photographs, video cameras and other
sensors. We have systems, which can reason with incomplete and
uncertain facts. Clearly, with these developments, much has been
accomplished since the advent of the digital computer.
In spite of these impressive achievements, we still have not been able to
produce coordinated, autonomous systems which posses some of the basic
abilities of a three-year old child. These include the ability to recognize and
remember numerous diverse objects in a scene, to learn new sounds and
associate them with objects and concepts and to adapt readily to many
diverse new situations. These are the challenges now facing researchers in
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AI. And they are not easy ones. They will require important breakthrough
before we can expect to equal the performance of our three-year old.
To gain a better understanding of AI, it is also useful to know what AI is not
for proper understanding of AI. AI is not the study and creation of
conventional computer systems. Even though one can agree that all
programs exhibit some degree of intelligence, an AI program will go beyond
this in demonstrating a high level of intelligence to a degree that equals or
exceeds the intelligence required of a human in performing some task. AI is
not the study of the mind or of the body or of languages as customarily
fond in fields of psychology, physiology, and cognitive science or linguistic.
To be sure, there are some overlap between these fields and AI. All seek a
better understanding of the human intelligence and sensing processes. But
in AI, the goal is to develop working computer systems that are truly
capable of performing tasks that require high levels of intelligence. The
programs are not necessarily meant to imitate human senses and thought
processes. Indeed, in performing some tasks differently, they actually
exceed human abilities. The important point is that the systems all be
capable of performing intelligent tasks effectively and efficiently.
Finally, a better understanding of AI is gained by looking at the component
areas of study that make up the whole. These includes such topics as
robotics, memory organization, knowledge representation, storage and
recall, learning models, inference techniques, commonsense reasoning,
dealing with uncertainty in reasoning and decision making, understanding
natural language, pattern recognition and machine vision methods, search
and matching, speech recognition and synthesis and a variety of AI tools.
How much success has been realized in AI to date?
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What are the next big challenges? The answer to these questions forms a
large part of the material covered in this text.
2.1 THE IMPORTANCE OF AI
AI may be one of the most important developments of the century. It will
affect the lives of most individuals in civilized countries by the end of the
century. And countries leading in the development of AI by then will
emerge as the dominant economic powers of the world.
The importance of AI becomes apparent to many of the worlds leading
countries during the later 1970’s. leaders in those countries who recognize
the potential for AI were willing to seek approval for long term commitment
for the needed to find intensive research programs in AI. The Japanese
were the first to demonstrate their commitment. They launched a very
ambitious program in AI research and development known as the Fifth
Generation, this plan was officially announced in October 1981. It calls for
implementation of a ten-year old plan to develop intelligent
supercomputers. It is a cooperative effort between government and private
companies having an interest in the manufacture of computer products,
robotics and related fields. With a combined budget of about one billion
dollars, the Japanese are determined. They will realize many of their goals,
namely, to produce systems that can converse in a natural language,
understand speech and visual scene, learn and refine their knowledge,
make decisions and exhibit other human traits. If they succeed and many
experts feel they will, their success as a leading economic power is
assured.
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Following the Japanese, other leading countries of the world have
announced plans for some of AI program. The British initiated a plan called
the Alvey project with a reputable budget. Their goals are not as ambitious
as the Japanese but are set to help British keep abreast and remain in the
race. The European common market countries have jointly initiated a
separate cooperative plan named ESPIRIT program. The French have their
own plan. Other countries including Canada, the Soviet Union, Italy, Austria
and even Irish Republic and Singapore have made some commitments in
funded research and development.
The United States, although well aware of the possible consequences, has
made no formal plan. However, steps have been taken by some
organization to push forward in AI research. First, there was the formation
of a consortium of private companies in 1983 to develop advanced
technologies that apply AI techniques (like VLSI). The consortium is known
as the Microelectronic and Computer technology Cooperation (MCC) and is
headquartered in Austin, Texas. Second, the Department of Defense
Advanced Research Projects Agency (DARPA) has increased its funding for
research in AI, including development support in three significant
programs;
1. Development of an autonomous Land vehicle (ALV) (a derivative
military vehicle).
2. Developments of pilots’ associate (an expert system, which provides
assistance to fighter pilots).
3. The strategic computing program (an AI based military
Supercomputer project).
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In addition, most of the larger high-tech companies such as IBM, DEC,
AT&T, Hewlett Packard, Texas Instrument, have their own research
programs. A number of smaller companies also have reputable research
programs.
One thing is clear, the future of a country is closely tied to the commitment
it is willing to make in funding research programs in AI.
As earlier explained, AI refers to computers that mimic aspects of human
thought. A simple electronic calculator doesn’t have AI. But a machine that
can learn from its mistakes or that can show reasoning power does have AI.
Between these extremes, there is no precise dividing line.
As computers have gotten more and more powerful, people have set higher
standards for AI. Things that were once thought of as AI are now quite
ordinary. And things that seem fantastic now will someday be just
humdrum. There is a tongue-in-cheek axiom about AI: Something is AI only
as long as it’s new and strange.
2.2 RELATIONSHIP WITH ROBOTICS
Artificial Intelligence tends itself to robotics. Scientists have dreamed for
over a century about building “Smart” androids, robots that look and act
like people. Androids already exist, but they aren’t very smart
If a machine has the ability to move around under its own power, to lift
things, and move things, it seems reasonable that it should do so with
some degree of “Smart”, if it is to be able to accomplish anything
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worthwhile. Otherwise it would be just a bumbling idiot box, and it might be
dangerous, like a driver less car with a brick on the pedal.
If a computer is to manipulate anything with it’s “brain power”, it will need
to be able to move around to grasp, to lift, and to carry objects. It might
contemplate fantastic exploits, but if it can’t act on its thoughts, the work
(and the risk) must be undertaken by people, whose strength and
maneuverability (and courage) are limited.
Robots without any intelligence, or electronic brains without moving parts,
have various uses and abilities. But when robots are given AI, their power
multipliers.
2.3 PROVING THEOREM
One measure of computer intelligence, that works on a level some where
between intuition and brute-force logic, is the proving of mathematical
theorems. If you have taken high-school geometry, you’ve probably been
exposed to theorem proving. Elementary logic courses deal with it too. And
computer programming is a type of reasoning similar to theorem proving.
Programs in AI have sometimes found remarkable proofs in mathematics.
ASIMOV’S THREE LAWS OF ROBOTICS
One of the worlds most well known Science fiction writers; ISAAC ASIMOV
invented the “three laws of Robotics” in 1942. He wrote more than 400
books in his lifetime. He was born in Russia in 1920, shortly after the
communist revolution, but did most of his work in United States.
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In one of his early science-fiction stories, Isaac ASIMOV first mentioned the
word ROBOTICS, along with the fundamental rules that all robots ought to
obey. The rules, now called ASIMOV’S three laws of Robotics, are as follows:
* A robot must not injure, or allow the injury of any human being.
· A robot must obey all orders from humans, except orders that would
contradict the first law.
· A robot must protect itself, except when to do so would contradict the
first law or the second law.
Although these rules were first coined in the 1940s, they are still
considered good standards for robots nowadays.
2.4 ASSEMBLY ROBOTS
An assembly robot is any robot that assembles products, such as cars,
home appliances and electronic equipment. Some assembly robots work
alone; most are used in automated integrated manufacturing systems
(AIMS), doing repetitive work at high speed and for along period of time.
Assembly robots have taken the place of human workers in some jobs.
Some people are concerned that robots take jobs from human beings. But
in fact, robots create new kind of jobs that are much more interesting than
the old ones.
A person who puts screws in a car door all day long. For example, might be
displaced by a robot. But that person might be trained to oversee the
operation of a set of assembly robots, to maintain the robots, to program
the robots computer, to check the quality of goods produced, or even to
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sell the goods themselves. The end result is a happier, better paid worker,
who is less likely to suffer from the boredom fatigue.
Many assembly robots take the form of robot arm. Several different joint
arrangements are used. The type of joint arrangement depends on the task
that the robot must perform. Joint arrangements are named according to
the type of coordinate system they follow. The complexity of motion in an
assembly robot is expressed in terms of the number of degrees of freedom.
One type of assembly robot developed in Japan is called the SCARA. It
resembles the Japanese folding screen that lets it move horizontally to
within 0.05 millimeter. Its implicity allows it to work at high speed, and also
minimizes the downtime, or time during which the device is out of
commission for repairs, It is also rather cheap, as far as assembly robots
go.
To do their jobs right, assembly robots need to have all the parts exactly in
place. They receive precise instructions, and there is almost no tolerance
for error. Human operators, on the other hand, can work with a much larger
margin for error. If you need to get a certain pair of pliers, you can
recognize it by its shape and size. A robot wouldn’t be able to find the
pliers unless it was exactly in the right place, or unless it was marked in
some way. There are some jobs, therefore, that assembly robot cannot do
very well. One of the biggest challenges for humans is the programming for
assembly robots, so that the efficiency will be greatest while minimizing the
possibility of “hang-ups”
2.5 AUTOMATED GUIDED VEHICLE (AGV)
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An AGV is a type of robot cart that runs without a driver. The cart has an
electric engine and is guided by a magnetic field, produced by a wire on or
just beneath the floor. Alternatively, an AGV might run on a track, like a
miniature train engine.
In an automated factory, AGVs are used to bring components to the
assembly lines. The parts must be put in just the right places, so the
assembly robots can find them.
In the future, the AGVs might serve as “low-priority” nurses in hospitals,
bringing food and nonessential items to patients. An AGV can also serve as
“a mechanical janitor” or “mechanical gopher”, performing routine chores
around the home or office.
On a larger scale, there has been some talk about making automobiles into
AGVs that follow wires embedded in the road pavement. This would take
the driver’s job away, letting components do it instead. Each car would
have it’s own individual computer, and the traffic in a whole city would be
overseen by one or more central computers. In the event of computer
failure, all traffic would stop. This will practically eliminate accidents. But
people might not accept the idea.
2.6 ELIZA
One of the most controversial developments in AI involved a program
called ELIZA. This program was put together in the 1960s by JOSEPH
WIZENBAUM of the Massachusetts Institute of Technology (MIT).
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The purpose of ELIZA was to simulate a psychoanalyst (a doctor who helps
people workout their problems by talking with them). The “patient” would
sit at a computer terminal and “converse” with the “doctor” by typing
sentences on a keyboard. The ELIZA program was infact, sometimes called
“DOCTOR”. Suppose you were the “patient”, and you sat down to the
computer to talk with ELIZA. You would see, on the screen:
SPEAK UP!
You might then type:
I’M UPSET.
The computer might then respond with:
WHY ARE YOU UPSET?
To which you might reply:
I DON’T KNOW. THAT’S WHY I’M HERE.
The conversation would then proceed, with ELIZA asking questions, and the
“patient” giving answers or asking other questions.
The program may never really commit itself by saying that’s wrong or don’t
ever do that again. The “doctor” would just make phrases, some from it’s
own memory and some stored from things the “patient” said earlier.
Nevertheless, ELIZA often behaves so much like a real psychiatrist that
some people actually suggested that it was just as good as human doctor.
Weizenbaum was disturbed by the reactions and the controversy ELIZA
caused. The program was not really very “smart” especially by standards of
the 1990s. The ELIZA program could not then, as computers still cannot,
have any feeling or concern of human beings.
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CHAPTER THREE
EXPERT SYSTEMS
3.0 WHAT IS EXPERT SYSTEM?
Expert Systems are a class of knowledge-based system. Knowledge-based
systems are computer programs which use knowledge of a subject, task,
user (or even knowledge about themselves) to do things like interpreting
speech or visual images; controlling a robot or a factory; advising on
decisions, or solving problems. Current expert systems are usually used as
specialist “consultants” for non-specialist users. They are primarily
concerned with making decisions as opposed to seeing, hearing etc. and
they typically interactive computer systems, not autonomous robots or
process controllers.
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During the interaction with an expert system, the user supplies information
about a problem, the expert system asks pertinent questions, and then
formulates suggestions or recommendations. In medicine for example, the
system may suggest possible diagnoses, plans of investigation, treatments
etc.
Expert system has risen to prominence recently and rapidly. In
consequence there is confusion about what is and what is not an expert
system. Expert Systems are programs that help to make decisions.
A characteristic of an expert system is that it should be able to provide
explanations of its decision-making methods.
It is often said that expert systems “mimics” the thought processes of
human expert (at least to a first approximation). There is, for example, an
emphasis on qualitative reasoning to arrive at a decision, in preference to
quantitative techniques. Often knowledge is represented with condition-
action rules, or semantic networks, both of which have been found by
psychologists to be good ways of modeling human knowledge.
Although they are important, the explanation feature and the attempt to
mimic human thought are not invariable in expert system. One feature
that in my view must be presented for a system to be called an expert
system is that the knowledge it uses is explicit; it is not implicit in some
abstract model or in the structure of the computer program. This idea of
“explicitness” is central to all knowledge –based systems.
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3.1 SUMMARY AND CONCLUSION
ECONOMIC EFFECTS OF ROBOTICS
Robots allow production of more goods at a lower cost than is possible
without them. If robot won’t breakdown, as often as human workers don’t
gets sick. robots can be used in dangerous jobs, saving human lives ( and
lowering medical bills). Robots can’t get bored, so they can do jobs that
would numb people’s mind with monotony. Many scientists and writers
think that the future success of industrialized economies will depend on
robotization. Nations that employ robots might prosper; nations that do not
use robots will never become major economic powers.
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All these great things are meaningless to the person who is out of work,
having been displaced by a robot. Sometimes such workers feel insulted as
well as injured. “ I was replaced by a machine”. This problem can be
solved, however, because robots help the economy more than they hurt it.
One solution would be to set up schools, paid for with some of the profits
resulting from robotization. These schools would retain people who have
been put out of work by robots, so they could find jobs that would make
better use of their human talents. This would in turn help the economy still
more and the people would be happier too.
As economies become less industrial and more information-based, AI, as
well as robotics, promises an expanding market of well-paid interesting
work. Ironically, robots and computers might be the key to making training
affordable to more people.
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