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.

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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