Dr's Chen History and application of AI

1. Ch1 AI:
History and Applications
Dr. Bernard Chen Ph.D.
University of Central Arkansas
Spring 2011

2. Outline
 AI History
 Overview of AI application areas

3. History
 There are two consequences of
mind/body analysis essential to the AI
enterprise:
 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

4. History: AI and the Rationalist
 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

5. History: Development of
Formal Logic
 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

6. History: the Turning Test
 The imitation game (1950)

7. Outline
 AI History
 Overview of AI application areas

8. AI application areas
 Game Playing
 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

9. AI application areas
 Automated reasoning and Theorem Proving
 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

10. AI application areas
 Expert System
 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 problem-
solving rules

11. AI application areas
 Expert System
 Current deficiencies:
 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

12. AI application areas
 Natural Language Understanding and
Semantics
 One of the long-standing goals of AI is the
creation of programming that are capable
of understanding and generating human
language

13. AI application areas
 Modeling Human Performance
 Capture the human mind (knowledge
representation)

14. AI application areas
 Robotics
 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

15. AI application areas
 Machine Learning
 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

16. Decision Tree Example

17. AI application areas
 Alternative representations
 Neural Networks
 Genetic Algorithm