Ch1 AI:
History and Applications
Dr. Bernard Chen Ph.D.
University of Central Arkansas
Spring 2011
Outline
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AI History
Overview of AI application areas
History
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There are two consequences of
mind/body analysis essential to the AI
enterprise:
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Mental processes have an existence of
their own, obey their own laws, and can be
studied in and of themselves
Once the mind and the body are
separated, philosophers found it necessary
to find a way to reconnect the two
History: AI and the Rationalist
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Modern research issues in AI are formed and
evolve through a combination of historical,
social and cultural pressures.
The rationalist tradition had an early
proponent in Plato, and was continued on
through the writings of Pascal, Descates, and
Liebniz
For the rationalist, the external world is
reconstructed through the clear and distinct
ideas of a mathematics
History: Development of
Formal Logic
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The goal of creating a formal language for
thought also appears in the work of George
Boole, another 19th century mathematician
whose work must be included in the roots of
AI
The importance of Boole’s accomplishment is
in the extraordinary power and simplicity of
the system he devised: Three Operations
History: the Turning Test
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The imitation game (1950)
Outline
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AI History
Overview of AI application areas
AI application areas
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Game Playing
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Much of the early research in state space search
was done using common board games such as
checkers, chess, and the 15-puzzle
Games can generate extremely large search
spaces. Theses are large and complex enough to
require powerful techniques for determining what
alternative to explore
AI application areas
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Automated reasoning and Theorem Proving
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Theorem-proving is one of the most fruitful
branches of the field
Theorem-proving research was responsible in
formalizing search algorithms and developing
formal representation languages such as predicate
calculus and the logic programming language
AI application areas
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Expert System
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One major insight gained from early work
in problem solving was the importance of
domain-specific knowledge
Expert knowledge is a combination of a
theoretical understanding of the problem
and a collection of heuristic problemsolving rules
AI application areas
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Expert System
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Current deficiencies:
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Lack of flexibility; if human cannot answer a
question immediately, he can return to an
examination of first principle and come up
something
Inability to provide deep explanations
Little learning from experience
AI application areas
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Natural Language Understanding and
Semantics
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One of the long-standing goals of AI is the
creation of programming that are capable
of understanding and generating human
language
AI application areas
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Modeling Human Performance
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Capture the human mind (knowledge
representation)
AI application areas
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Robotics
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A robot that blindly performs a sequence of
actions without responding to changes or
being able to detect and correct errors
could hardly considered intelligent
It should have some degree of sensors and
algorithms to guild it
AI application areas
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Machine Learning
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Learning has remained a challenging area
in AI
An expert system may perform extensive
and costly computation to solve a problem;
unlike human, it usually don’t remember
the solution
Decision Tree Example
AI application areas
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Alternative representations
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Neural Networks
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Genetic Algorithm
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