Artificial Intelligence
Applications.
Applications of
Artificial Intelligence:-
1.Problem Solving
2.Game Playing
3.Theorem Proving
4.Natural Language
Processing & Understanding
5.Perception General
· Speech Reorganization
· Pattern Reorganization
6.Image Processing
7.Expert System
8.Computer Vision
9.Robotics
10.Intelligent Computer
Assisted Instruction
11.Automatic programming
12.Planning & Decision
Support systems
13.Engineering Design
& Comical Analysis
14. Neural Architecture.
15. Heuristic
Classification.
1 Problem Solving:-
This
is the first application area of AI research., the objective of this particular
area of research is how to implement the procedures on AI systems to solve the
problems like Human Beings.
2 :- Game Playing:-
Much of early research in state space
search was done using common board games such as checkers, chess and 8 puzzle.
Most games are played using a well defined set of rules. This makes it easy to
generate the search space and frees the researcher from many of the ambiguities
and complexities inherent in less structured problems. The board Configurations
used in playing these games are easily represented in computer, requiring none
of complex formalisms. For solving large and complex AI problems it requires
lots of techniques like Heuristics. We commonly used the term intelligence
seems to reside in the heuristics used by Human beings to solve the problems.
3 :- Theorem Proving:-
Theorem proving is another application
area of AI research., ie. To prove Boolean Algebra theorems as a humans we
first try to prove Lemma., i.e it tell us whether the Theorem is having
feasible solution or not. If the theorem having feasible solution we will try
to prove it otherwise discard it., In the same way whether the AI system will
react to prove Lemma before trying to attempting to prove a theorem., is the
focus of this application area of research.
4 Natural Langauge
understading:-
The main goal of this problem is we can
ask the question to the computer in our mother tongue the computer can receive
that particular language and the system gave the response with in the same
language. The effective use of a Computer has involved the use off a
Programming Language of a set of Commands that we must use to Communicate with
the Computer. The goal of natural language processing is to enable people and
language such as English, rather than in a computer language.
It can be divided in to Two sub fields.
Natural Language Understanding :
Which investigates methods of allowing the Computer to improve instructions
given in ordinary English so that Computers can understand people more easily.
Natural Language Generation :
This aims to have Computers produce ordinary English language so that people an
understand Computers more easily.
5. Perception:-
The process of perception is usually
involves that the set of operations i.e. Touching , Smelling Listening ,
Tasting , and Eating. These Perceptual activities incorporation into
Intelligent Computer System is concerned with the areas of Natural language
Understanding & Processing and Computer Vision mainly. The are two major
Challenges in the application area of Perception.
1. Speech Reorganization
2. Pattern Reorganization
¨Speech Reorganization:-
The main goal of this problem is how
the Computer System can recognize our Speeches. (Next process is to understand
those Speeches and process them i.e. Encoding & Decoding i.e producing the
result in the same language.) Its one is very difficult; Speech Reorganization
can be described in two ways.
1. Discrete Speech
Reorganization
Means People can interact with the
Computer in their mother tongue. In such interaction whether they can insert
time gap in between the two words or two sentences (In this type of Speech
Reorganization the computer takes some time for searching the database).
2. Continues Speech
Reorganization
Means when we interact with the
computer in our mother tongue we can not insert the time gap in between the two
words or sentences , i.e. we can talk continuously with the Computer (For this
purpose we can increase speed of the computer).
¨Pattern Reorganization: -
this the computer can identify
the real world objects with the help of “Camera”. Its one is also very difficult
, because
- To identify the regular shape
objects, we can see that object from any angle; we can imagine the actual shape
of the object (means to picturise which part is light fallen) through this we
can identify the total structure of that particular object.
-To identify the irregular shape
things, we can see that particular thing from any angle; through this we cannot
imagine the actual structure. With help of that we can attach the Camera to the
computer and picturise certain part of the light fallen image with the help of
that whether the AI system can recognize the actual structure of the image or
not? It is some what difficult compare to the regular shape things, till now
the research is going on. This is related the application area of Computer
Vision.
A Pattern is a quantitative or
structured description of an object or some other entity of interest of an
Image. Pattern is found an arrangement of descriptors. Pattern recognition is
the research area that studies the operation and design of systems that
recognize patterns in data. It encloses the discriminate analysis, feature
extraction, error estimation, cluster analysis, and parsing (sometimes called
syntactical pattern recognition). Important application areas are image
analysis, character recognition, speech recognition and analysis, man and
machine diagnostics, person identification and industrial inspection.
Closely Related Areas Pattern
Recognition
Artificial Intelligence
Expert systems and machine
learning
Neural Networks
Computer Vision
Cognition
Perception
Image Processing
6.Image Processing:-
Where as in pattern reorganization we
can catch the image of real world things with the help of Camera. The goal of
Image Processing is to identify the relations between the parts of image.
It is a simple task to attach a Camera
to a computer so that the computer can receive visual images. People generally
use Vision as their primary means of sensing their environment. We generally
see more than we here. i.e. how can we provide such perceptual facilities
touch, smell, taste, listen, and eat to the AI System. The goal of Computer
Vision research is to give computers this powerful facility for understanding their
surroundings. Currently, one of the primary uses of Computer Vision is in the
area of Robotics.
Ex: - We can take a
Satellite image to identify the roots and forests; we can make digitize all the
image and place on the disk. With the help of particular scale to convert the
image in to dots form, later we can identify that particular image at any time.
Its one is time consuming process. With the help of “ image processing” how to
reduce the time to process an image till now the AI research will be continuously
going on.
In Image Processing the process of
image recognition can be broken into the following main stages.
· Image capture
· Edge detection
· Segmentation
· Recognition and Analysis.
Image capturing can be performed by a
simple Camera, which converts light signals from a scale of electrical
signals., i.e., done by human visual system. We obtained these light signals in
a set of 0’s and 1’s. Each pixel takes on one of a number of possible values
often from 0 to 255. Color images are broken down in the same way, but with
varying colors instead of gray scales. When a computer receives an image from
sensor in form of set of pixels. These pixels are integrated to give the
computer an understanding of what it is perceiving.
An image has been obtained, is to
determine where the edges are in the image, the very first stage of analysis is
called edge detection. Objects in the real world are almost all have solid
edges of one kind or another, detecting those images is first step in the
process of determining which objects are present in a scene.
Once the edges have been detected, in
an image, this information can be used to Segment the image, into homogeneous
areas. There are other methods available for segmenting an image, apart from
using edge detection, like threshold method. This method involves finding the
color of each pixel in an image and considering adjacent pixels to be in the
same area as long as their color is similar enough.
A similar method for segmenting images
is splitting and merging. Splitting involves taking an area that is not
homogeneous and splitting it into two or more smaller areas, each of which is
homogeneous. Merging involves taking two areas that are the same as each other,
and adjacent to each other and combining them together into a large area. This
provides a sophisticated interactive approach to segmenting an image.
Intermediate Level of processing
Low Level Processing High Level
Processing
7.§Expert system:-
Expert means the person who had complete knowledge in particular field, ie is
called as an expert. The main aim of this problem is with the help of experts,
to load their tricks on to the compute and make available those tricks to the
other users. The expert can solve the problems with in the time.
The goal of this problem is how to load
the tricks and ideas of an expert on to the computer, till now the research
will be going on.
8. § Computer Vision:-
It is a simple task to attach a camera to a computer so that the computer can
receive visual images. People generally use vision as their primary means of
sensing their environment. We generally see more than we here, feel, smell, or
taste.
The goal of computer vision research is
to give computers this powerful facility for understanding their surroundings.
Currently, one of the primary uses of computer vision is in the area of
Robotics.
9. § Robotics:-
A robot is an electro – mechanical device
that can be programmed to perfume manual tasks. The robotics industries
association formally defines to move a Robot as a “ Programmable
multi-functional manipulator designed to move material, parts, tools, or
specialized devices through variable programmed motions for the performance of
variety of tasks”.
Not all robotics is considered to be
part of AI. A Robot that perform sonly the actions that it is has been
pre-programmed to perform is considered to be a “dumb” robot, includes some
kind of sensory apparatus, such as a camera , that allows it to respond to
changes in its environment , rather than just to follow instructions
“mindlessly”.
10. § Intelligent Computer
– Assisted Instruction:-
Computer - Assisted Instruction (CAI)
has been used in bringing the power of the computer to bear on the educational
process. Now AI methods are being applied to the development of intelligent
computerized “ Tutors” that shape their teaching techniques to fit the leaning
patterns of individual students.
11. § Automatic
Programming:- Programming is the process of telling
the computer exactly what we want to do . the goal of automatic programming is
to create special programs that act as intelligent “Tools” to assist
programmers and expedite each phase of the programming process. The ultimate
aim of automatic programming is a computer system that could develop programs
by itself, in response to an in according with the specifications of the
program developer.
12. § Planning and Decision Support system:- When we have a goal, either we rely on luck and providence to achieve that goal or we design and implement a plan. The realization of a complex goal may require to construction of a formal and detailed plan. Intelligent planning programs are designed to provide active assistance in the planning process and are expected to the particularly helpful to managers with decision making responsibilities.
13. §Engineering Design
& Camical Analysis:-
Artificial Intelligence applications
are playing major role in Engineering Drawings & Camical analysis to design
expert drawings and Camical synthesis.
14. § Neural
Architecture:-
People or more intelligent than
Computers,. But AI researchers are trying how make Computers Intelligent.
Humans are better at interpreting noisy input, such as recognizing a face in a
darkened room from an odd angle. Even where human may not be able to solve some
problem, we generally can make a reasonable guess as to its solution. Neural
architectures, because they capture knowledge in a large no. of units. Neural
architectures are robust because knowledge is distributed somewhat uniformly
around the network.
Neural architectures also provide a
natural model for parallelism, because each neuron is an independent unit. This
showdown searching the data base a massively parallel architecture like the
human brain would not suffer from this problem.
15. § Heuristic
Classification:-
The term Heuristic means to Find &
Discover., find the problem and discover the solution. For solving complex AI
problems it’s requires lots of knowledge and some represented mechanisms in
form of Heuristic Search Techniques., i.e refered to known as Heuristic
Classification.
