Vs Intelligent Computing
computing is used by Von Neumann computers . These are the machines
that follows an already described set of instructions to process
approach to problem solving. These
task at a time. The
coded in program and data structures.
way the problem is to solved is
machines are completely
is due to a software or hardware fault.
With The Conventional Computers :-
able to recognise patterns.
able to manage the different
kind of data
obtained in the real world.
to express the “how and
why” questions in
operate within the parameters that were programmed into it.
intelligent system is a machine interconnected to other computers
having capacity to gather and analyse 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.
goal of intelligent computing is to understand the principles that
make intelligent behaviour 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 artefacts.
techniques used in intelligent computing are neural
networks, fuzzy logic, genetic algorithms, and intelligent agents,
will be able to do jobs that require detailed instructions
alertness and decision making capabilities
injuries and stress to human beings
playing by making them more realistic.
applications that perform repetitive tasks.The
concept of intelligent agent is central in intelligent
An agent perceives its environment through percept and acts through
actuators. Agents can improve their performance through learning.
agents, model-based agents, goal- based agents, utility-based agents
achieving the desired goal is not enough. We may look for quicker,
safer, cheaper trip to reach a destination.
happiness should be taken into consideration. We call it utility.
utility function is the agent’s performance measure
of the uncertainty in the world, a utility agent choses
action that maximizes the expected utility.
the current state of the environment is not enough. The agent needs
program combines the goal information with the envi- ronment model
to choose the actions that achieve that goal.
the future with “What will happen if I do A?”
as knowledge supporting the decisions is explicitly rep-
and can be modified.
element: responsible for making improvements
element: responsible for selecting external ac-
It is what we considered as agent so far.
How well is the agent is doing w.r.t. a fixed perfor-
generator: allows the agent to explore.
have advantage that they first in the unknown environments and then
learn form their doings. The learning element in these uses feedback
form ‘critic’ on how the agent is doing and decides how the
performance element should be modified to do better in the future.
computing and conventional computing are not competing 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.