Artificial Intelligence presentation

2. “The analytical
engine has no
pretensions what
ever to originate
any thing it can do
what ever we
know how to order
it to perform “

3. Intelligence is the
ability to acquire,
retrieve knowledge
in a meaningful
way

4. Artificial intelligence (AI) is
the intelligence of machines
and the branch of computer
science that aims to create it.

the study and design of intelligent
agents" where an intelligent
agent is a system that perceives its
environment and takes actions that
maximize its chances of success.

5. What makes a computer
intelligent.:
 Speed of computation
Filteration of results
Algorithms:

6. Research in AI has focused on
following components:
LEARNING
REASONING:
UNDERSTANDING
CREATIVITY:
INTUITION:

7. Why artificial
intelligence:
•trouble understanding specific
situations and adapting to new
situations.
•improves machine behavior

8. KNOWLEDGE REPRESENTATION:
facilitates inferencing
use a symbol system to represent a
domain of discourse
give meaning to the sentences in
the logic.

9. EXAMPLE:
CANNIBAL-MISSIONARY PROBLEM
the importance of knowledge.
solved by intelligent algorithms

10. NEED FOR FORMAL LANGUAGES:
“The boy saw a girl with a
telescope”
Symbolic logic is a syntactically
unambigious knowledge
representation language

11. KNOWLEDGE REPRESENTATION
TECHNIQUES IN AI:
PROPOSITIONAL LOGIC
declarative statement
~ -> Negation
→ -> implication
↔ -> implies and implied by
v -> disjunction
^ -> Conjunction

12. SYNTAX:
syntax= how a sentence looks like
Sentence -> AtomicSentence | ComplexSentence
AtomicSentence -> T(RUE) | F(ALSE) | Symbols
ComplexSentence -> ( Sentence ) | NOT Sentence |
Connective -> AND | OR | IMPLIES | EQUIV(ALENT)
Precedence: NOT AND OR IMPLIES EQUIVALENT
conjunction disjunction implication equivalence
negation

13. Semantics:
semantics= what a sentence means
interpretation:
assigns each symbol a truth value, either
t(rue) or f(alse)
the truth value of T(RUE) is t(rue)
the truth value of F(ALSE) is f(alse)

14. Terminology:
A sentence is valid if it is True under all
possible assignments of
True/False to its propositional variables (e.g.
P_:P)
 Valid sentences are also referred to as
tautologies

15. Semantic Networks:
l Graph structures that encode taxonomic
knowledge of objects and their properties.
– objects represented as nodes
– relations represented as labeled edges
l Inheritance = form of inference in which
subclasses inherit properties of
superclasses

16. .Frames:
Distinguish
– statements about an object’s
relationships
– properties of the object

17. NORMAL Form in predicate LOGIC
Rule:-
1. Replace and by using equivalent
formulas.
2. Repeated use of negation
~(~p)=F.Demorgan’s law to bring negation in
front of each atom.
~ (GF)= ~G~F.Use ~x F(x)= x~F(x) and
~xF(x) = x~F(x)
Then use all the equivalent expressions to
bring the quantities in front of the expressions

18. Resolution in predicate LOGIC:
i) R(a)
ii) R(x) M(x,b)
First replace a in place of x in 2nd premise and
conclude M(a,b).
e.g:
Marcus was a man. Man (marcus)
Marcus was a Pompeian. Pompeian (Marcus)
Caesar was a ruler. Ruler (Caesar)

19. Nonmonotonic Reasoning:
Collection of true facts never
decreases
Facts changes with time

20. Principles of NMRs :
 If x is not known, then conclude y
If x cannot be proved, then conclude y
e.g. 1: To build a program that generates a
solution to a fairly a simple problem.
e.g. 2: To find out a time at which three busy
can all attain a meeting
dependency-directed backtracking

21. Necessity of NMR:
 The presence of incomplete information
requires default reasoning.
 A changing world must be decided by a
changing database.
 Generating a complete solution to a
problem may require temporary assumption
about partial solution.

22. PROCEDURAL Vs DECLARATIVE
KNOWLEDGE:
Advantages of declarative knowledge are:
 The ability to use knowledge in ways that
the system designer did not forsee
Advantages of procedural knowledge are:
 Possibly faster usage

24. Fundamental Problems of AI
limited acquisition of information
by itself
encodable in “information
structures”

25. CONCLUSION:
Finally we are clear about the
vast spread of the artificial
intelligence in various fields and
the area of knowledge
representation in artificial
intelligence.

27. THANK YOU