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A Multidisciplinary Journal of Scientific Research & Education, 2(4), April-2016, Volume: 2, Issue: 4
Artificial Intelligence and Robotics
Ms. Swapnali R Ghadge
Department of Computer Engineering,
Padmbhushan Vasantdada Patil College of Engineering,
Sion, Mumbai-09, Maharashtra
Email: [email protected]
Abstract: Artificial Intelligence is a branch of Science which deals with helping machines finds solutions to complex problems
in a more human-like fashion. Artificial intelligence is exhibited by artificial (man- made, non-natural, manufactured)entity, a
system is generally assumed to be a computer. AI systems are now in routine use in economics, medicine, engineering and the
military, as well as being built into many common home computer software applications, traditional strategy games like
computer chess and other video games. Artificial Intelligence has come a long way from its early roots, driven by dedicated
researchers This generally involves borrowing characteristics from human intelligence, and applying them as algorithms in a
computer friendly way. A more or less flexible or efficient approach can be taken depending on the requirements established,
which influences how artificial the intelligent behavior appears. Intelligence involves mechanisms, and AI research has
discovered how to make computers carry out some of them and not others. If doing a task requires only mechanisms that are well
understood today, computer programs can give very impressive performances on these tasks. Such programs should be
considered ``somewhat intelligent''. It is related to the similar task of using computers to understand human intelligence. Humans
throughout history have always sought to mimic the appearance, mobility, functionality, intelligent operation, and thinking
process of biological creatures. This field of biologically inspired technology, having the moniker biometrics, has evolved from
making static copies of human and animals in the form of statues to the emergence of robots that operate with realistic
appearance and behavior.
Keywords: EAP, artificial muscles, artificial intelligence, biometrics
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I. INTRODUCTION
The term Artificial Intelligence (AI) mean "the science and
engineering of making intelligent machines”. Although AI has
a strong science fiction connotation, it forms a vital branch of
computer science, dealing with intelligent behavior, learning
and adaptation in machines. Research in AI is concerned with
producing machines to automate tasks requiring intelligent
behavior. AI is generally associated with Computer Science,
but it has many important links with other fields such as
Math’s, Psychology, Cognition, Biology and Philosophy,
among many others. Our ability to combine knowledge from all
these fields will ultimately benefit our progress in the quest of
creating an intelligent artificial being. AI systems are now in
routine use in economics, medicine, engineering and the
military, as well as being built into many common home
computer software applications, traditional strategy games like
computer chess and other video games.
II.
TECHNOLOGY
There are many different approaches to Artificial Intelligence,
none of which are either completely right or wrong. Some are
obviously more suited than others in some cases, but any
working alternative can be defended.
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Over the past five decades, AI research has mostly been
focusing on solving specific problems. Numerous
solutions have been devised and improved to do so
efficiently and reliably. This explains why the field of
Artificial Intelligence is split into many branches, ranging
from Pattern Recognition to Artificial Life, including
Evolutionary Computation and Planning.
III. ARTIFICIAL LIFE THROUGH ROBOTICS:
 Laws of Robotics:
1. A robot may not injure a human being or, through
inaction, allow a human being to come to harm.
2. A robot must obey the orders given it by human beings
except where such orders would conflict with the first
law.
3. A robot must protect its own existence as long as such
protection does not conflict with the first or second law.
Robotics has been an evolution of the field of automation
where there was a desire to emulate biologically inspired
A Multidisciplinary Journal of Scientific Research & Education, 2(4), April-2016, Volume: 2, Issue: 4
characteristics of manipulation and mobility. In recent
years, significant advances have been made in robotics,
artificial intelligence and others fields allowing to make
sophisticate biologically inspired robots [Bar-Cohen and
Brea zeal]. Using these advances, scientists and engineers
are increasingly reverse engineering many animals'
performance characteristics. Biologically inspired
robotics is a subset of the interdisciplinary field of
biomimetic. Technology progress resulted in machines
that can recognize facial expressions, understand speech,
and perform mobility very similar to living creatures
including walking, hopping, and swimming. Further,
advances in polymer sciences led to the emergence of
artificial muscles using Electro active Polymer (EAP)
materials that show functional characteristics remarkably
similar to biological muscles.
established infrastructure. For this purpose, it is necessary
to develop comprehensive understanding of EAP
materials' behavior, as well as effective processing,
shaping and characterization techniques. The technology
of artificial muscles is still in its emerging stages but the
increased resources, the growing number of investigators
conducting research related to EAP, and the improved
collaboration among developers, users, and sponsors are
leading to a rapid progress.

Biometric robots using EAP:
Mimicking nature would significantly expand
the functionality of robots allowing performance of tasks
that are currently impossible. As technology evolves,
great number of biologically inspired robots actuated by
EAP materials emulating biological creatures is expected
to emerge. The challenges to making such robots can be
seen graphically in Figure 2 where humanlike and doglike robots are shown to hop and express joy. Both tasks
are easy for humans and dogs to do but are extremely
complex to perform by existing robots.

Artificial Muscles:
Muscles are the key to the mobility and
manipulation capability of biological creatures and when
creating biomimetic it is essential to create actuators that
emulate muscles. The potential to make such actuators is
increasingly becoming feasible with the emergence of the
electro active polymers (EAP), which are also known as
artificial muscles [Bar-Cohen, 2001]. These materials
have functional similarities to biological muscles,
including resilience, damage tolerance, and large
actuation strains. Moreover, these materials can be used to
make mechanical devices with no traditional components
like gears, and bearings, which are responsible to their
high costs, weight and premature failures. The large
displacement that can be obtained with EAP using low
mass, low power and, in some of these materials also low
voltage, makes them attractive actuators. The capability
of EAPs to emulate muscles offers robotic capabilities
that have been in the realm of science fiction when
relying on existing actuators.
FIGURE 2: Making a joyfully hopping human-like and
dog-like robots actuated by EAP materials are great
challenges for biomimetic robots
FIGURE 3: An android head and a robotic hand that are
serving as biomimetic platforms for the development of
artificial muscles.
FIGURE 1: A graphic illustration of the grand challenge
for the development of EAP actuated robotics – an arm
wrestling match against human.

Biologically inspired robots:
The evolution in capabilities that are inspired by
biology has increased to a level where more sophisticated
and demanding fields, such as space science, are
considering the use of such robots. At JPL, a six-legged
robot is currently being developed for consideration in
future missions to such planets as Mars. Such robots
include the LEMUR (Limbed Excursion Mobile Utility
Robot). This type of robot would potentially perform
The EAP materials that have been developed so far are
still exhibiting low conversion efficiency, are not robust,
and there are no standard commercial materials available
for consideration in practical applications. In order to be
able to take these materials from the development phase
to application as effective actuators, there is a need for an
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A Multidisciplinary Journal of Scientific Research & Education, 2(4), April-2016, Volume: 2, Issue: 4
mobility in complex terrains, sample acquisition and
analysis, and many other functions that are attributed to
legged animals including grasping and object
manipulation. This evolution may potentially lead to the
use of life-like robots in future NASA missions that
involve landing on various planets including Mars.
The details of such future missions will be designed as a
plot, commonly used in entertainment shows rather than
conventional mission plans of a rover moving in a terrain
and performing simple autonomous tasks. Equipped with 
multifunctional tools and multiple cameras, the LEMUR 
robots are intended to inspect and maintain installations
beyond humanity's easy reach in space with the ability to
operate in harsh planetary environments that are
hazardous to human. This spider looking robot has 6 legs,
each of which has interchangeable end-effectors to
perform the required mission (see Figure 4). The axis
symmetric layout is a lot like a starfish or octopus, and it
has a panning camera system that allows omni-directional
movement and manipulation operations.




FIGURE 4: A new class of multi-limbed robots called
LEMUR (Limbed Excursion Mobile Utility Robot) is
under development at JPL
IV. MAJOR BRANCHES OF AI







Perceptive system
A system that approximates the way a human
sees, hears, and feels objects
Vision system
Capture, store, and manipulate visual images and
pictures
Robotics
Mechanical and computer devices that perform
tedious tasks with high precision
Expert system
Stores knowledge and makes inferences
Learning system
Computer changes how it functions or reacts to
situations based on feedback
Natural language processing
Computers understand and react to statements
and commands made in a “natural” language,
such as English.
Neural network
Computer system that can act like or simulate the
functioning of the human brain.







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V. CATEGORIES OF AI
AI divides roughly into two schools of thought:
Conventional AI.
Computational Intelligence (CI)
Conventional AI :Conventional AI mostly involves methods now classified
as machine learning, characterized by formalism and
statistical analysis. This is also known as symbolic AI,
logical AI, neat AI and Good Old Fashioned Artificial
Intelligence (GOFAI).
Methods include:
Expert systems: apply reasoning capabilities to reach a
conclusion. An expert system can process large amounts
of known information and provide conclusions based on
them.
Case based reasoning
Bayesian networks
Behavior based AI: a modular method of building AI
systems by hand.
Computational Intelligence (CI) :Computational
Intelligence
involves
iterative
development or learning (e.g. parameter tuning e.g. in
connectionist systems). Learning is based on empirical
data and is associated with non-symbolic AI, scruffy AI
and soft computing.
Methods include:
Neural networks: systems with very strong pattern
recognition capabilities.
Fuzzy systems: techniques for reasoning under
uncertainty, has been widely used in modern industrial
and consumer product control systems.
Evolutionary computation: applies biologically inspired
concepts such as populations, mutation and survival of the
fittest to generate increasingly better solutions to the
problem. These methods most notably divide into
evolutionary algorithms (e.g. genetic algorithms) and
swarm intelligence (e.g. ant algorithms).
A. Typical problems to which AI methods are applied:Pattern recognition
o Optical character recognition
o Handwriting recognition
o Speech recognition
o Face recognition
Natural language processing, Translation and Chatter bots
Non-linear control and Robotics
Computer vision, Virtual reality and Image processing
A Multidisciplinary Journal of Scientific Research & Education, 2(4), April-2016, Volume: 2, Issue: 4

place after the repeated application of the above
operators. Artificial evolution (AE) describes a process
involving individual evolutionary algorithms; EAs are
individual components that participate in an AE.
Game theory and Strategic planning
VI. CLASSIFICATION OF ARTIFICIAL INTELLIGENCE
Classified by design
• Symbolic AI – Designers explicitly program all
of the AI “knowledge.”
• Connectionist AI – Designers “teach” an
artificial neural network what the AI needs to
“know.”
• Evolutionary AI – Designers give the AI the
ability to refine itself.
VII. APPLICATION
 Robotics:Robotics is the engineering science and technology of
robots, and their design, manufacture, application, and
structural disposition. Robotics and AI are often seen as
two different fields entirely, robotics being a mechanical
engineer in field, and AI being computer science related.
 Roboboa an artificial dancing python has sensing
and small logical capabilities.
Symbolic AI:Symbolic AI is a very attractive area of AI with its focus
on the Mind's symbolic processing. Perhaps this is
because our Minds, besides being very important to us,
are both exceedingly familiar and yet also scientifically
mysterious. The challenge of having to build mental
systems that behave intelligently is an ultimate challenge
to many scientists.
Computer Scientists use their minds (some of the
time at least!) to program the computer with instructions
So it can execute tasks. AI programmers write programs
to enable the computer to work out itself to various
degrees as to what to do. They aim to leave more of their
minds on the machine than with 'ordinary' programs.
 Strength:
 Working with logical problems.
 Weakness:
 Working with imperfect data.

Connectionist AI:Connectionist AI systems are large networks of extremely
simple numerical processors, massively interconnected
and running in parallel. There has been great progress in
the connectionist approach, and while it is still unclear
whether the approach will succeed, it is also unclear
exactly what the implications for cognitive science would
be if it did succeed.
 Strength:
 Working with imperfect data.
 Weakness:
 Working with logical problems.
Data Mining
•
A application that allows to the
computer to learn from its environment
•
Eg. Smart Cars, self-parking cars can
parallel park themselves without hitting
any other objects around it.
 Medical:Diagnostic programs
Doctors input the symptoms of the patience and the AI
helps decide what medications are best.
 Disease symptoms
 Medical history
 Test results of a patient
Evolutionary AI:In artificial intelligence, an evolutionary algorithm (EA)
is a subset of evolutionary computation, a generic
population-based met heuristic optimization algorithm.
An EA uses mechanisms inspired by biological evolution,
such as reproduction, mutation, recombination, and
selection. Candidate solutions to the optimization problem
play the role of individuals in a population, and the fitness
function determines the quality of the solutions (see also
loss function). Evolution of the population then takes
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A Multidisciplinary Journal of Scientific Research & Education, 2(4), April-2016, Volume: 2, Issue: 4
 Video Games:AI You can buy machines that can play master level chess
for a few hundred dollars. There is some AI in them, but
they play well against people mainly through brute force
computation--looking at hundreds of thousands of
positions. What you do in the game will determine how
the computer reactsx. An enemy see’s your character; the
computer’s reaction is to attack your character.

Natural Language Understanding, which
investigates methods of allowing the computer to
comprehend instructions given in ordinary
English so that computers can understand people
more easily.
Expert Systems:-
Related Discipline:
In artificial intelligence, an evolutionary algorithm (EA)
is a subset of evolutionary computation, a generic
population-based metaheuristic optimization algorithm.
An EA uses mechanisms inspired by biological evolution,
such as reproduction, mutation, recombination, and
selection. Candidate solutions to the optimization problem
play the role of individuals in a population, and the fitness
function determines the quality of the solutions (see also
loss function). Evolution of the population then takes
place after the repeated application of the above
operators. Artificial evolution (AE) describes a process
involving individual evolutionary algorithms; EAs are
individual components that participate in an AE. An
Expert System is software that attempts to provide an
answer to a problem, or clarify uncertainties .Carisma is
an advanced human interactive AI Expert systems are
designed to take the place of human experts, while others
are designed to aid them.
Linguistics - study of language and of languages
•
Psycholinguistics - language and the mind,
models of human language processing
•
Neurolinguistics language processing
•
Logic - unambiguous formal language used in
representing (unambiguous) meanings
neural-level
models
of
 Neural Network :Artificial Neural Networks tries to simulate the structure
and/or functional Aspects of biological neural networks.
Neural Networks are mathematical constructs that
emulate the processes people use to recognize patterns,
learn tasks, and solve problems.
 Speech Recognition :In the 1990s, computer speech recognition reached a
practical level for limited purposes. Thus United Airlines
has replaced its keyboard tree for flight information by a
system using speech recognition of flight numbers and
city names. It is quite convenient. On the other hand,
while it is possible to instruct some computers using
speech, most users have gone back to the keyboard and
the mouse as still more convenient.
Fig :- Expert Systems

•
Natural Language Processing:-
 Understanding Natural Language:Just getting a sequence of words into a computer is not
enough. Parsing sentences is not enough either. The
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A Multidisciplinary Journal of Scientific Research & Education, 2(4), April-2016, Volume: 2, Issue: 4
computer has to be provided with an understanding of the
domain the text is about, and this is presently possible
only for very limited domains.
[4]. Buchanan, Bruce G.. Timeline: A Brief History of
Artificial
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http://www.aaai.org/AITopics/pmwiki/pmwiki.php/A
ITopics/BriefHistory

Computer Vision:The world is composed of three-dimensional
objects, but the inputs to the human eye and computer’s
TV cameras are two dimensional. Some useful programs
can work solely in two dimensions, but full computer
vision requires partial three-dimensional information that
is not just a set of two-dimensional views. At present
there are only limited ways of representing threedimensional information directly, and they are not as
good as what humans evidently use.
VIII.
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CONCLUSION
We conclude that if the machine could successfully
pretend to be human to a knowledgeable observer then
you certainly should consider it intelligent. AI systems are
now in routine use in various field such as economics,
medicine, engineering and the military, as well as being
built into many common home computer software
applications, traditional strategy games etc.
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707,Jul
1992
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AI is an exciting and rewarding discipline. AI is branch
of computer science that is concerned with the
automation of intelligent behavior. The revised
definition of AI is - AI is the study of mechanisms
underlying
intelligent
behavior
through
the
construction and evaluation of artifacts that attempt to
enact those mechanisms. So it is concluded that it work
as an artificial human brain which have an unbelievable
artificial thinking power.
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(Eds.),“Biologically-Inspired
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