Artificial Intelligence | Page 2

are important here.

4. The fourth view of AI is that it is th e study of rational agents. This view deals
with building machines that act rationa lly. The focus is on how the system acts
and performs, and not so much on the reas oning process. A rational agent is one
that acts rationally, that is, is in the best possible manner.

1.1.2 Typical AI problems
While studying the typical range of tasks that we might expect an “intelligent entity” to
perform, we need to consider both “com mon-place” tasks as well as expert tasks.
Examples of common-place tasks include

– Recognizing people, objects.
– Communicating (through natural language).
– Navigating around obstacles on the streets

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These tasks are done matter of factly and routinely by peop le and some other animals.

Expert tasks include:
• Medical diagnosis.
• Mathematical problem solving
• Playing games like chess

These tasks cannot be done by all people , and can only be performed by skilled
specialists.

Now, which of these tasks are easy and whic h ones are hard? Clearly tasks of the first
type are easy for humans to perform, and al most all are able to master them. The second
range of tasks requires skill development and/or intelligence and only some specialists
can perform them well. However, when we look at what computer systems have been
able to achieve to date, we see that thei r achievements include performing sophisticated
tasks like medical diagnosis, performing sym bolic integration, proving theorems and
playing chess.

On the other hand it has proved to be very ha rd to make computer systems perform many
routine tasks that all humans and a lot of an imals can do. Examples of such tasks include
navigating our way without running into thi ngs, catching prey and avoiding predators.
Humans and animals are also capable of in terpreting complex sensory information. We
are able to recognize ob jects and people from the visual image that we receive. We are
also able to perform co mplex social functions.

Intelligent behaviour
This discussion brings us back to the question of what constitutes intelligent behaviour.
Some of these tasks and applications are:
� Perception involving image recogn ition and computer vision
� Reasoning
� Learning
� Understanding language involving natural la nguage processing, speech processing
� Solving problems
� Robotics

1.1.3 Practical Impact of AI
AI components are embedded in numerous de vices e.g. in copy machines for automatic
correction of operation for copy quality improve ment. AI systems are in everyday use for
identifying credit card fraud, for advising docto rs, for recognizing speech and in helping
complex planning tasks. Then there are intelligent tutoring systems that provide students
with personalized attention
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Thus AI has increased understanding of the nature of intelligence and found many
applications. It has helped in the understanding of human reasoning, and of the nature of
intelligence. It has also helped us unders tand the complexity of modeling human
reasoning.

1.1.4 Approaches to AI
Strong AI aims to build machines that can truly reason and solve problems. These
machines should be self aware and their overall intellectual ability needs to be
indistinguishable from that of a human be ing. Excessive optimism in the 1950s and 1960s
concerning strong AI has given way to an a ppreciation of the extreme difficulty of the
problem. Strong AI maintains that suitab ly programmed machines are capable of
cognitive mental states.

Weak AI: deals with the creation of some form of computer-based artificial intelligence
that cannot truly reason and solve problems, but can act as if it were intelligent. Weak AI
holds that suitably programmed machin es can simulate human cognition.

Applied AI: aims to produce commercially viable "smart" systems such as, for example,
a security system that is able to recognise the faces of people who are permitted to enter a
particular building. Applied AI has al ready enjoyed considerable success.

Cognitive AI: computers are used to test theories about how the human mind works--for
example, theories about how we recognise faces and other objects, or about how we solve
abstract problems.

1.1.5 Limits of AI Today
Today’s successful AI systems operate in well-defined domains and employ narrow,
specialized knowledge. Common sense knowledge is needed to function in complex,
open-ended worlds. Such a system also needs to understand unconstrained natural
language. However these capabilities are not yet fully present in today’s intelligent
systems.

What can AI systems do

Today’s AI systems have been able to achieve limited success