The document discusses key concepts in artificial intelligence and knowledge representation. It defines AI as the intelligence demonstrated by machines, and notes its goals include creating intelligent agents that can perceive their environment and take actions to maximize success. It also summarizes techniques for knowledge representation in AI like semantic networks, frames, predicate logic, and nonmonotonic reasoning. The document emphasizes that knowledge representation facilitates inferencing and the need for formal languages to avoid ambiguity.

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