2. Origin
• Human wanted to see themselves.
• They started constructing their statues.
• The desire to make model intelligent originated.
• Babbage’s analytical engine .
• Thermo ionic valves ,transistors ,IC’s and VLSI
engines gave way to first to fourth generations
computes.
• Fifth generation – intelligence.
3. Introduction
• No formal definition to date.
• Compared with human intelligence.
• Intelligence Immeasurable.
• Intelligence helps to identify right knowledge
at right time.
• The system capable of doing this is called
rational.
4. Problem solving approaches AI
• State is solution at a given step .
• State space approach.
• To solve :one state next state
• Example: four puzzle problem
• 3 blocks digits
• 1 block blank
• AIM : To reach from given state to goal state.
7. • Thus AI problem no straightforward
mathematical or logical algorithm available .
• Solved by intuitive approach.
• Some of these well known algorithm to solve
AI are
I. Generate and test
II. Hill climbing
III. Heuristic search
IV. Means and ends analysis
8.
9. Subject of AI
• Started with game playing and theorem
proving
• Topics significant to under currently :
I. Learning system
II. Knowledge representation and reasoning
III. Planning
IV. Knowledge acquisition
V. Fuzzy logic
VI. ANN
10. LEARNING SYSTEM
•Learning of pronunciation
by a child from his mother.
•Child tries to imitate
•Inductive learning is about
generalizations .e.g. all
birds fly
•Analogy based learning
e.g. the motion of electron
from planetary motion.
11. Knowledge representation and
reasoning
• To reach a pre defined
goal state from given
initial state.
• Lesser the number of
states higher is the
efficiency.
• Knowledge base
techniques
12. Knowledge acquisition
• Generation of knowledge from given
knowledge base.
• Automated acquisition is active field .
• Soft computing remarkable ability of
human mind to reason and learn in an
environment of uncertainty and imprecision.
• Collection of fuzzy logic ,artificial neural nets
,genetic algorithms ,belief calculus etc.
13. Fuzzy logic & ANN
• fuzzy sets and logical connectives.
• Members have value 1 and other elements of
universal set 0.
• Common operators AND ,OR and negation.
• ANN are electrical analogues of biological neural
nets.
• ANN is collection of artificial neurons .
• The unsupervised learning algorithm have been
applied in AI techniques.
