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Conclusion |
| << Advanced Topics |
Artificial
Intelligence (CS607)
Search
though the internet and read
about intersting happeneing
and reseach
going on
around the globe in the
are of clustering.
http://www.elet.polimi.it/upload/matteucc/Clustering/tutorial_html/
The above
link might be useful to
explore knowledge about
clustering.
10
Conclusion
We have
now come to the end of this
course and we have tried to
cover all the
core
technologies of AI at the basic
level. We hope that the
set of topics we have
studied so
far can give you the
essential base to work into
specialized, cutting-
edge
areas of AI.
Let us
recap what have we studied
and concluded so far. The list of
major topics
that we
covered in the course
is:
Introduction to
intelligence and AI
·
Classical
problem solving
·
Genetic
algorithms
·
Knowledge
representation and reasoning
·
Expert
systems
·
Fuzzy
systems
·
Learning
·
Planning
·
Advanced
topics
·
Let us
review each of them very
briefly.
10.1
Intelligence and AI
Intelligence is
defined by some characteristics
that are common in
different
intelligent
species, including problem
solving, uncertainty handling,
planning,
perception,
information processing, recognition,
etc.
AI is classified
differently by two major
schools of thought. One
school classifies
AI as study of
systems that think like
humans i.e. strong AI and
the other
classifies AI as
study of systems that act
like humans i.e. weak AI.
Most of the
techniques
prevalent today are counted
in the latter
classification.
10.2 Problem
solving
Many people
view AI as nothing but
problem solving. Early work
in AI was done
around
the generic concept of
problem solving, starting
with the basic
technique
of generate
and test. Although such
classical problem solving
did not get
extraordinary
success but still it
provided a conceptual backbone
for almost each
approach to
the systematic exploration of
alternatives.
The basic
technique used in classical
problem solving is searching.
There are
several
algorithms for searching for
problem solving, including
BFS, DFS, hill
climbing,
beam search, A* etc. broadly
categorized on the basis of
completeness,
optimality and
informed ness. A special
branch of problem solving
through
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Artificial
Intelligence (CS607)
searching
involved adversarial problems
like classical two-player
games, handled
in classical
problem solving by adversarial
search algorithms like
Minimax.
10.3
Genetic Algorithms
Genetic
algorithms is a modern advancement to
the hill climbing search
based
problem
solving. Genetic algorithms
are inspired by the
biological theory of
evolution and
provide facilities of parallel
search agents using
collaborative hill
climbing. We
have seen that many
otherwise difficult problems to
solve through
classical
programming or blind
search techniques are
easily but
undeterministically
solved using genetic
algorithms.
At this
point we introduced the
cycle of AI to set base for
systematic approach to
study
contemporary techniques in AI.
10.4
Knowledge representation and
reasoning
Reasoning
has been presented by most
researchers in AI as the core
ability of an
intelligent
being. By nature, reasoning is
tightly coupled with
knowledge
representation
i.e. the reasoning process
must exactly know how
the knowledge
is kept to
manipulate and extract new
knowledge from it.
As we are
yet to decode the exact
representation of knowledge in
natural
intelligent
beings like humans, we have
based our knowledge
representation and
hence
reasoning on man-made logical
representation namely logic
i.e. predicate
logic
and family.
10.5 Expert
systems
The first
breakthrough successful application of AI
came from the subject
of
knowledge
representation and reasoning
and was name expert
systems. Based
on its
components i.e. knowledge
base, inference and working
memory, expert
systems
have been successfully
applied to diagnosis, interpretation,
prescription,
design,
planning, simulations,
etc.
10.6 Fuzzy
systems
Predicate
logic and the classical and
successful expert systems
were limited in
that
they could only deal
with perfect boolean logic
alone. Fuzzy logic
provided
the
new base of knowledge and
logic representation to capture
uncertain
information and
thus fuzzy reasoning systems
were developed. Just like
expert
systems,
fuzzy systems have almost
recently found exceptional
success and are
one of the
most used AI systems of
today, with applications
ranging from self-
focusing
cameras to automatic intelligent
stock trading
systems.
10.7
Learning
Having
covered the core
intelligence characteristic of reasoning,
we shifted to the
other
major half contributed to AI
i.e. learning or formally
machine learning. The
KRR and
fuzzy systems perform
remarkably but they cannot
add or improve their
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Artificial
Intelligence (CS607)
knowledge at
all, and that is where
learning was felt essential
i.e. the ability of
knowledge
based systems to improve
through experience.
Learning
has been categorized into
rote, inductive and deductive
learning. Out of
these
all almost all the
prevalent learning techniques
are attributed to
inductive
learning,
including concept learning,
decision tree learning and
neural networks.
10.8
Planning
In the end we
have studied a rather
specialized part of AI namely
planning.
Planning is
basically advancement to problem
solving in which concepts of
KRR
are
fused with the knowledge of
classical problem solving to
construct advanced
systems to
solve reasonably complex
real world problems with
multiple,
interrelated and
unrelated goals. We have
learned that using predicate
logic and
regression,
problems could be elegantly
solved which would have
been
nightmare
for machines in case of
classical problem solving
approach.
10.9 Advanced
Topics
You have
been given just a hint of
where the field of AI is
moving by mentioning
some of
the exciting areas of AI of
today including vision,
robotics, soft-computing
and clustering. Of
these we saw robotics as the
most comprehensive field
in
which
the other topics like
vision can be considered as a
sub-part.
Now,
it's up to you to take these
thoughts and directions
along with the
basics
and move
forward into advanced study
and true application of the
field of Artificial
Intelligence.
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