Conventional embedded, Internet-connected computer that has theConventional embedded, Internet-connected computer that has the

Conventional
Computers

Conventional
computers also known as Von Neumann computers or classical machines
are machines that follow a well described set of instructions to
process data.
These
use
a cognitive approach to problem solving; the way the problem is to
solved must be known and stated in small unambiguous instructions. .
The
system tries
to solve a
problem
in a straight forward manner i.e.
sequentially
, meaning it
does one task at a time..The
problem
expertise
is encoded in both program and data structures.
The
machines are totally predictable; if anything goes wrong is due to a
software or hardware fault.

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Major
components of conventional computers are

memory
processing
and
bandwidth

Problems

pattern
recognition

specifically,
it is very difficult to program a classical computer between
different microorganisms.

Conventional
computing is often unable to manage the variability of data obtained
in the real world.

The
systems are are
incapable to express the “how, why” questions.

A
conventional computer could only operate within the parameters that
were programmed into it. It could have all the processor speed and
memory, but the code would be what software designers and writers
have produced.

Intelligent
Computers

An
intelligent system is a machine with an embedded, Internet-connected
computer that has the capacity to gather and analyze data and
communicate with other systems. Other criteria for intelligent
systems include the capacity to learn from experience, security,
connectivity, the ability to adapt according to current data and the
capacity for remote monitoring and management.

The
central scientific goal of computational intelligence is to
understand the prin- ciples that make intelligent behavior possible,
in natural or artificial systems. The main hypothesis is that
reasoning is computation. The central engineering goal is to specify
methods for the design of useful, intelligent artifacts.

Advantages

machines
will be able to do jobs that require detailed instructions

mental
alertness and decision making capibilities

less
injuries and stress to human beings

Applicaitons

game
playing by making them more realistic.

Speech
recognition

Understanding
natural language

Machine
learning

Agents
and environments

• Agent:
An agent
is anything that can be viewed as:
– perceiving its
environment through
sensors and

acting upon
that environment
through actuators.

Intelligent
agents

The
concept of intelligent agent is central in AI.

AI
aims to design intelligent agents that are useful, reactive,

autonomous
and even social and pro-active.

An
agent perceives its environment through percept and acts through
actuators.

A
performance measure evaluates the behavior of the agent.

An
agent that acts to maximize its expected performance mea-

sure
is called a rational agent.

PEAS:
A task environment specification that includes Perfor- mance
measure, Environment, Actuators and Sensors.

Agent
= Architecture
+ Program

Intelligent
agents

Four
types of agents: Reflex agents, model-based agents, goal- based
agents, and utility-based agents.

Agents
can improve their performance through learning.

This
is a high-level present of agent programs.

States
representations: atomic, factored, structured. Increas- ing
expressiveness power.

Utility-based
agents

Sometimes
achieving the desired goal is not enough. We may look for quicker,
safer, cheaper trip to reach a destination.

Agent
happiness should be taken into consideration. We call it utility.

A
utility function is the agent’s performance measure

Because
of the uncertainty in the world, a utility agent choses

the
action that maximizes the expected utility.

Goal-based
agents

Knowing
the current state of the environment is not enough. The agent needs
some goal
information.

Agent
program combines the goal information with the envi- ronment model
to choose the actions that achieve that goal.

Consider
the future with “What will happen if I do A?”

Flexible
as knowledge supporting the decisions is explicitly rep-

resented
and can be modified.

Learning
agents

• Programming
agents by hand can be very tedious. “Some more expeditious method
seem desirable” Alan Turing, 1950.

• Four
conceptual components:

–  Learning
element: responsible for making improvements

–  Performance
element: responsible for selecting external ac-

tions.
It is what we considered as agent so far.

–  Critic:
How well is the agent is doing w.r.t. a fixed perfor-

mance
standard.

–  Problem
generator: allows the agent to explore.

Learning
Agents

Learning
agents has an advantage that it allows the agents to initially
operate in unknown environements and to become more competent than
its initial knowledge alone might allow.

The
most important disticiton is between the learning element which is
responsible for making improvments and the performance element which
is responsible for selecting external actions.
The
learning element uses feedback form ‘critic’ on how the agent is
doing and determines how the performance element should be modified
to do better in the future.

INTELLIGENT
AGENTS
What
are Intelligent Agents: are
applications that perform repetitive tasks, without being managed by
a human.  The agents are
also referred to as “robots”, “bots”, “crawlers” and
“worms”.  A popular use for the software is
shopping.  Shopping bots automatically check web-sites for
the best prices.  Then alert the user through email about
the best price.  Also, another popular use for agents are
from websites such as Monster.  At
Monster, the bots automatically check the job posts weekly.  After
the bots or agent finds a post that matches a description from the
job seeker, the program alerts the user through email. This prevents
the job seeker from check continuously. The agent saves the use a
large amount of time.

Distinguishing
factors for agents: These factors mostly have to do with
intelligent and not intelligent agents.  How well the agent
can adapt to fit the users needs. Also, how well the agent can adapt
based off its performance.  If search results are not
yielding pertinent information, the searching software has to choose
another strategy.  Moreover, how pro active the agent is.
For example email the user after the agent found a
target.    Additionally, how user-friendly the
agent is. Lastly, distinguished factor is function the agent is
performing.
Example
of Shopping bots: The
bots on this page search out product and price information from
online stores and then report back, by email, to the user.   The
agents can adapt.  The focus can be on customer reviews to
shipping needs.  By delegating the agent to check prices
for example, the user can spend time on school, becoming more
efficient.
(http://dir.yahoo.com/Business_and_Economy/Shopping_and_Services/Retailers/Virtual_Malls/Shopping_Agents/).  

Other
Application for Agents: 1, email filter 2, appointments
scheduler 3, cheap air-fair finder 4, stock screeners 5, job finders
6, ect.

Conclusion

Neural
networks and conventional algorithmic computers are not in
competition but complement each other. There are tasks are more
suited to an algorithmic approach like arithmetic operations and
tasks that are more suited to neural networks. Even more, a large
number of tasks, require systems that use a combination of the two
approaches (normally a conventional computer is used to supervise the
neural network) in order to perform at maximum efficiency.