ARTIFICIAL INTELLIGENCE
ARTIFICIAL INTELLIGENCE
Contents
: Abstract
: Introduction
: History
: Concepts
: Branches of AI
: Artificial Intelligence in fiction
: Problems o 7.1 Deduction, reasoning, problem solving o 7.2 Knowledge representation o 7.3 Planning o 7.4 Learning o 7.5 Motion & manipulation o 7.6 Perception o 7.7 Social intelligence o 7.8 Creativity o 7.9 General intelligence
VIII : Tools o 8.1 Search & optimization o 8.2 Logic o 8.3 Probabilistic methods for uncertain reasoning o 8.4 Neural Networks o 8.5 Control Theory o 8.6 Languages
IX : Philosophy
X
: Applications of Artificial Intelligence
XI : Conclusion
XII : References
I
II
III
IV
V
VI
VII
I: Abstract
At the beginning of the Stone Age, when people started taking shelters in caves, they made attempts to immortalize themselves by painting their images on rocks. With the gradual progress in civilization, they felt interested to see themselves in different forms. So, they started constructing models of human being with sand, clay and stones. The size, shape, constituents and style of the model humans continued evolving but the man was not happy with the models that only looked like him. He had a strong desire to make the model ‘intelligent’, so that it could act and think as he did. This, however, was a much more complex task than what he had done before. So, he took millions of years to construct an ‘analytical engine’ that could perform a little arithmetic mechanically. Babbage’s analytical engine was the first significant success in the modern era of computing. Computers of the first generation, which were realized following this revolutionary success, were made of thermo-ionic valves. They could perform the so-called
‘number crunching’ operations. The second-generation computers came up shortly after the invention of transistors and were more miniaturized in size. They were mainly used for commercial data processing and
Contents
: Abstract
: Introduction
: History
: Concepts
: Branches of AI
: Artificial Intelligence in fiction
: Problems o 7.1 Deduction, reasoning, problem solving o 7.2 Knowledge representation o 7.3 Planning o 7.4 Learning o 7.5 Motion & manipulation o 7.6 Perception o 7.7 Social intelligence o 7.8 Creativity o 7.9 General intelligence
VIII : Tools o 8.1 Search & optimization o 8.2 Logic o 8.3 Probabilistic methods for uncertain reasoning o 8.4 Neural Networks o 8.5 Control Theory o 8.6 Languages
IX : Philosophy
X
: Applications of Artificial Intelligence
XI : Conclusion
XII : References
I
II
III
IV
V
VI
VII
I: Abstract
At the beginning of the Stone Age, when people started taking shelters in caves, they made attempts to immortalize themselves by painting their images on rocks. With the gradual progress in civilization, they felt interested to see themselves in different forms. So, they started constructing models of human being with sand, clay and stones. The size, shape, constituents and style of the model humans continued evolving but the man was not happy with the models that only looked like him. He had a strong desire to make the model ‘intelligent’, so that it could act and think as he did. This, however, was a much more complex task than what he had done before. So, he took millions of years to construct an ‘analytical engine’ that could perform a little arithmetic mechanically. Babbage’s analytical engine was the first significant success in the modern era of computing. Computers of the first generation, which were realized following this revolutionary success, were made of thermo-ionic valves. They could perform the so-called
‘number crunching’ operations. The second-generation computers came up shortly after the invention of transistors and were more miniaturized in size. They were mainly used for commercial data processing and
References: Russell & Norvig (2003) (who prefer the term "rational agent") and write "The whole-agent view is now widely accepted in the field" (Russell & 4. Buchanan, B. G. and Shortliffe, E. H., Eds., Rule-Based ExpertSystems, Addison-Wesley, Reading, MA, 1984. 5. Bezdek. J. C., Ed., Pattern Recognition with Fuzzy Objective Function Algorithms, Kluwer Academic Press, 1991.