Artificial Intelligence - Ayushix - ushak

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Nov 27, 2013 (3 years and 11 months ago)

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Ayushi Pradhan

ARTIFICIAL INTELLIGENCE

A property of machines that, if achieved, mimics human thought processes.
Many researchers in artificial intelligence consider the abilities of perception,
learning, reasoning, and decision making as essential to claims of machines
possessing artificial intelligence.

ARTIFICIAL INTELLIGENCE

While some techniques within computational intelligence are often counted as
artificial intelligence techniques (e.g., genetic algorithms, or neural networks)
there is a clear difference between these techniques and traditional, logic based
artificial intelligence techniques. In general, typical artificial intelligence
techniques are top
-
to
-
bottom, where the structure of models, solutions, etc. is
imposed from above. Computational intelligence techniques are generally
bottom
-
up, where order and structure emerges from an unstructured
beginning. The
areas covered by the term computational intelligence are also
known under the name

soft computing
.


COMPUTATIONAL
INTELLIGENCE

Systems
dependent on artificial intelligence would normally require more
processing capabilities than normal systems.


REQUIREMENTS OF ARTIFICIAL
INTELLIGENCE

While speed and memory of an AI system is important intelligent systems
quite often require a range of sensors to receive input data from the
environment. Output devices will include the normal peripheral devices such
as printers and monitors but may also include a range of

actuators

or speech
synthesis devices.

Robotics

are one application of intelligent
systems and are
used in

CAM systems.

HARDWARE

AI is dependent not only on sufficient hardware but also on the software to
run the hardware and to
synthesize
the data received. Once the data has been
received and processed the AI system needs to make an intelligent response.
To create this software non
-
procedural languages are often used. These
include languages such as LISP and PROLOG. Both of these languages will
actually allow the system to learn and modify its responses to its environment.

SOFTWARE


Models and Simulations
: Models and simulations may require high
graphics capabilities (3D photorealism) and be capable of processing high
end mathematical models which can be very CPU intensive. Machines like
this will require:

a fast CPU

large amounts of RAM

A good graphics card

large storage capacity (i.e. large hard drive)

may require
specialized
input output devices

will require
specialized
software

and may require an AI language such as PROLOG or LISP


SPECIFIC REQUIREMENTS


Neural Networks
: A wide range of software is available for developing
neural networks, some can even be used as plugins for spreadsheets. The
requirements will vary according to the use but a neural network would
normally store large amounts of data and have the data linked in a
configuration which would require a large storage capacity hard drive, fast
access to the data would also be an advantage. If the network uses graphics
then a fast graphics adapter would also be required
.


Expert Systems
: Expert systems are
specialised

information systems
which would normally require a large storage capacity and fast processing
capability. Many of these will be used via the internet so a fast connection
would also be necessary

Search plays a major role in solving many


(AI) problems. Search is a universal
problem
-
solving mechanism in AI. In many problems, sequence of steps
required to solve is not known in advance but must be determined by
systematic trial
-
and
-
error exploration of alternatives.

The problems that are addressed by AI search algorithms fall into three
general classes:


AI TECHNIQUES

1 Searching:

Classic
examples in the AI literature of path
-
finding problems are sliding
-
title
puzzles, Rubik’s Cube and theorem proving. The single
-
title puzzles are
common test beds for research in AI search algorithms as they are very simple
to represent and manipulate. Real
-
world problems include the traveling
salesman problem, vehicle navigation, and the wiring of VLSI circuits. In each
case, the task is to find a sequence of operations that map an initial state to a
goal state.


1. SINGLE
-
AGENT
PATH
-
FINDING
PROBLEMS

Two
-
players
games are two
-
player perfect information games. Chess, checkers,
and
Othello
are some of the two
-
player games.


2. TWO
-
PLAYERS GAMES

3. CONSTRAINT SATISFACTION
PROBLEMS

Eight Queens problem is the best example. The task is to place eight queens
on an 8*8 chessboard such that no two queens are on the same row, column
or diagonal. Real
-
world examples of constraint satisfaction problems are
planning and scheduling applications.


A branch of artificial intelligence concerned with the classification or
description of observations. Pattern recognition aims to classify data
(patterns) based on either a priori knowledge or on statistical information
extracted from the patterns. The patterns to be classified are usually groups of
measurements or observations, defining points in an appropriate
multidimensional space.

AI TECHNIQUES

2

Pattern Recognition:

It is the research area that studies the operation and design of systems that
recognize patterns in data. It encloses sub disciplines like discriminant
analysis, feature extraction, error estimation, cluster analysis (together
sometimes called statistical pattern recognition), grammatical inference and
parsing (sometimes called syntactical pattern recognition). Important
application areas are image analysis, character recognition, speech analysis,
man and machine diagnostics, person identification and industrial inspection.

Heuristic search is an

AI

search technique that employs heuristic for its
moves.

Heuristic

is a rule of thumb that probably leads to a solution. Heuristics
play a major role in search strategies because of exponential nature of the
most problems. Heuristics help to reduce the number of alternatives from an
exponential number to a polynomial number. In

Artificial Intelligence
,
heuristic
search

has a general meaning, and a more specialized technical
meaning. In a general sense, the term heuristic is used for any advice that is
often effective, but is not guaranteed to work in every case. Within the
heuristic search architecture, however, the term heuristic usually refers to the
special case of a

heuristic evaluation function
.

AI TECHNIQUES

3 Heuristics:

In
order to solve larger problems, domain
-
specific knowledge must be added
to improve search efficiency. Information about the problem include the
nature of states, cost of transforming from one state to another, and
characteristics of the goals. This information can often be expressed in the
form of

heuristic evaluation function, say f( n, g), a function of the nodes n
and/or the goals g.



HEURISTIC
INFORMATION

To solve problems computers require intelligence. Learning is central to
intelligence. As intelligence requires knowledge, it is necessary for the
computers to acquire knowledge. Machine learning serves this purpose.

Machine learning

refers to a system capable of acquiring and integrating the
knowledge automatically. The capability of the systems to learn from
experience, training, analytical observation, and other means, results in a
system that can continuously self
-
improve and thereby exhibit efficiency and
effectiveness.

A

machine learning system

usually starts with some knowledge and a
corresponding knowledge organization so that it can interpret, analyze, and
test the knowledge acquired.


AI TECHNIQUES

4 Machine Learning:

Game playing:
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. To beat a world champion by brute force and known reliable heuristics
requires being able to look at 200 million positions per second.

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.

Understanding Natural Language:
Just
getting a sequence of words into a
computer is not enough
.
The 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
.

APPLICATIONS OF AI

Computer Vision:
The
world is composed of three
-
dimensional objects, but
the inputs to the human eye and computers' 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
three
-
dimensional information directly, and they are not as good as what
humans evidently use.

Heuristic Classification:
One
of the most feasible kinds of expert system
given the present knowledge of AI is to put some information in one of a
fixed set of categories using several sources of information. An example is
advising whether to accept a proposed credit card purchase. Information is
available about the owner of the credit card, his record of payment and also
about the item he is buying and about the establishment from which he is
buying it (e.g., about whether there have been previous credit card frauds at
this establishment).


Expert Systems:
A
``knowledge engineer'' interviews experts in a certain
domain and tries to embody their knowledge in a computer program for
carrying out some task. How well this works depends on whether the
intellectual mechanisms required for the task are within the present state of
AI. When this turned out not to be so, there were many disappointing results.
One of the first expert systems was MYCIN in 1974, which diagnosed
bacterial infections of the blood and suggested treatments. It did better than
medical students or practicing doctors, provided its limitations were observed.
Namely, its ontology included bacteria, symptoms, and treatments and did not
include patients, doctors, hospitals, death, recovery, and events occurring in
time. Its interactions depended on a single patient being considered. Since the
experts consulted by the knowledge engineers knew about patients, doctors,
death, recovery, etc., it is clear that the knowledge engineers forced what the
experts told them into a predetermined framework. In the present state of AI,
this has to be true. The usefulness of current expert systems depends on their
users having common sense.


SOME IMPORTANT TERMS & THEIR DEFINITIONS

A test for deciding whether a computer is intelligent, proposed in 1950 by the
mathematician Alan Turing. He preferred to consider if machines can be
intelligent as opposed to whether "they can think". In a Turing test, a human
converse in writing with an unseen person or machine. If the human cannot
distinguish between an unseen human and an unseen machine (computer)
then the machine is said to have passed the test and is intelligent.


TURING TEST

In the basic Turing Test, there are three

terminal

s. Two of the terminals are
operated by humans, and the third terminal is operated by a computer. Each
terminal is physically separated from the other two. One human is designated
as the questioner. The other human and the computer are designated the
respondents. The questioner interrogates both the human respondent and the
computer according to a specified format, within a certain subject area and
context, and for a preset length of time (such as 10 minutes). After the
specified time, the questioner tries to decide which terminal is operated by the
human respondent, and which
terminal
is operated by the computer. The test
is repeated many times. If the questioner makes the correct determination in
half of the test runs or less, the computer is considered to have artificial
intelligence, because the questioner regards it as "just as human" as the human
respondent.

A CAPTCHA (Completely Automated Public

Turing test

to tell Computers
and Humans Apart) is a

challenge
-
response system

test designed to
differentiate humans from automated programs. A CAPTCHA differentiates
between human and

bot

by setting some task that is easy for most humans to
perform but is more difficult and time
-
consuming for current bots to
complete.

CAPTCHAs are often used to stop bots and other automated programs from
using
blogs to
affect search engine rankings, signing up for e
-
mail accounts to
send out

spam

or take part in on
-
line polls.



CAPTCHA

Frequently, a CAPTCHA features an image file of slightly
distorted

alphanumeric

character
s. A human can usually read the characters in
the image without too much difficulty. A bot program is able to recognize that
the content contains an image , but it has no idea what the image is. To
accommodate
the visually
-
impaired, some CAPTCHAs use audio files. In such
a system, the human listens to a series of letters or short words and types
what he hears to prove he is not a bot.

Language that is close to the everyday speech of human beings.


NATURAL LANGUAGE


Machine Learning


An set of techniques in

artificial intelligence

that makes it
possible for a machine's performance to improve based
on feedback from previous performances.

Neural nets, networks of distributed, parallel processing computer systems
based on the structure and functioning of the human brain


NEURAL NETWORK


Pattern Recognition


The
ability to identify the underlying patterns in
input
data.

Generalization
or rule of thumb that directs someone's attention in a way that
helps them learn or discover e.g. if you are having trouble understanding a
problem, try drawing a diagram of it. Programming in a heuristic way is a
major skill in building

artificial intelligence.
Instead of
rigorous mathematical
algorithms, it involves general strategies. Good anti
-
virus software uses
heuristic detection
-

spotting patterns of
behavior
that "aren't quite right"
instead of just relying on
recognizing
virus code


HEURISTICS

A type of logic used to process conditions that are only partly true or false.
It
deals
with probabilities instead of the more classic Boolean logic which can
only deal with TRUE and FALSE. Practical applications in computer
controlled systems include the control of fuel and air mixtures in internal
combustion engines, the proportional slowing of the speed of objects as they
approach a given state or target, the heating and cooling of objects or spaces
to prevent overheating and the mixing of two or more ingredients to achieve a
defined final condition (especially when the components and their properties
are constantly changing). Fuzzy logic uses weighted algorithms in computer
programs to simulate human thought or life
-
like responses to external
conditions.


FUZZY LOGIC

Fuzzy logic in it's simplest terms expands the
dichotomy
of true or not true to
include a range of answers in between. The usual example is say instead of being
black or white, fuzziness allows for shades of gray. Since fuzzy logic allows this
extra bandwidth in fuzzy answers, fuzzy rules used in programming can cover a
much broader area. A fuzzy rule such as "When it rains, you get wet"*** can cover
a lot of ground. It would be able to several
instances
of itself such as "when it
rains

a lot
, you get wet

a lot
" or "when it rains

a little
, you get wet

a little
".

Rules like this are beautiful because they are human rules. They are a much better
model of how we think. It is not often that questions that arise in life have
bivalent answers. There are a few that do such as "Are you married?". Other
questions such as "Do you like your job?", would tend to elicit a range of a
response falling somewhere between yes and no.

How exactly is a fuzzy rule able to cover so much ground? By the use of a patches.
A fuzzy rule will define a fuzzy patch. Say for example that you would like to use
fuzzy logic to control an air
conditioner.
You could define a fuzzy set for the
temperature
range as COLD, COOL, JUST RIGHT, WARM and HOT. A system
could be composed of a few sloppy rules with wide patches, or many precise rules
with narrow patches. Perhaps the air conditioner system is representative of other
real systems. That is, an optimal solution involves some wide sloppy rules, and
some precise ones.


The fuzzy rules that would go with the air conditioner system would be:

Rule 1:

If the temperature is cold, them motor speed stops.

Rule 2:

If the temperature is cool, the motor speed slows.

Rule 3:

If the temperature is just right, the motor speed is medium.

Rule 4:

If the
temperature
is warm, the motor speed is fast.

Rule 5:

If the temperature is hot, the motor speed blasts.


This fuzzy system works well because the patches will cover lines that
correspond to relations between temperature and motor speed if they are
non
-
linear and squiggle. In contrast a similar bivalent system might be built of
many specific rules such as if
temperature
is 60 degrees than the motor speed
is 50. Patches developed from rules like this would only be points, and the
system developed from it would only define a collection of points
--

not a
terrific model. Therein lies a greatness of fuzziness.


A software agent is
a program
that acts on
behalf
of the user or another
program and therefore has some authority to change its
behavior
depending
on circumstance. It must therefore have some
degree
of

artificial intelligence.
They might ask questions and respond to commands in different ways
according to a user's work patterns, or use
reasoning
to set goals for a user.


AGENTS
-

HELP OR
HINDRANCE?

The increasing use of robotics, artificial intelligence (AI) and expert systems
raises a range of ethical issues.

For example, at which point should humans hand over key decision
-
making to
a computer? Should
robots have
the same rights as humans? What social
impacts might arise with the replacement of human
workers or
the creation
of smart weapons?

A FEW ISSUES

http://
www
-
formal.stanford.edu/jmc/whatisai/node4.html

http://
www.digitalist.org/Itgs/theory/12_ai_expsys/12_ai_expsys.htm

http://www.cs.vu.nl/ci
/

http://
searchsecurity.techtarget.com/definition/CAPTCHA

http://
intelligence.worldofcomputing.net/ai
-
search/ai
-
search
-
techniques.html

http://
aaai.org/AITopics/PatternRecognition

http://
intelligence.worldofcomputing.net/machine
-
learning/machine
-
learning
-
overview.html

http://
mark.stosberg.com/Tech/fuzzy/role_in_ai.html

http://www.bettscomputers.com/requirementsofai.htm

BIBLIOGRAPHY