The document discusses knowledge representation in artificial intelligence. It defines knowledge as information used to solve problems, including facts, concepts, procedures, models and examples. Knowledge representation schemes include declarative, procedural, inheritable, inferential, relational and heuristic knowledge. Facts can be represented internally in a computer using logic or rules. Propositional calculus and predicate logic are also discussed as methods for knowledge representation, along with inference rules and predicates. The key aspects of knowledge acquisition and creation are outlined.

1. Prepared by:
Ekta Dagar

2.  The most important use of AI is to develop expert
systems to help human beings for solving real world
problems easily, effectively and efficiently.
 To develop such expert systems, Knowledge and
Knowledge based systems are needed.

3. Knowledge
 What is knowledge?
 Knowledge is the sort of information that people use to
solve problems.
 It is the basis of differentiation between an ordinary
human and an expert.
 It can be defined as a body of facts and principles
accumulated by human beings.

4.  Knowledge includes:
 facts, concepts, procedures, models, heuristics,
examples.
 Basic components of Knowledge:
 Set of data
 Form of belief or hypothesis
 Kind of information

5. Knowledge-Set of Data
 Knowledge is different from data as data is a raw form
of observations whereas knowledge is an organized
form of data and procedures which can be used for
some useful purposes.
 Knowledge requires use of data and information.

6. Difference between Knowledge base and
database
DATABASE KNOWLEDGE BASE
Collection of data representing facts Has information at a higher level of
abstraction
Operates on a single object Operates on a class of objects
Maintained for operational purposes Used for data analysis and planning
Represented by relational/hierarchical
or network model
Represented by logic or rules
Updates are performed by clerical
personal
Updates are performed by domain
experts

7. Knowledge-Belief
 Belief is any meaningful expression that can be
expressed as either true or false.
 Hypothesis is a belief that is not known to be true.
 Knowledge is true justified belief.

8. Knowledge-Information
 Information is the processed form of data.
 If information is capable of creating more information
and can become some part of an action, then it falls in
the category of knowledge.
 Knowledge is information about objects, concepts and
relationships that are assumed to exist in a particular
area of interest.

9. Knowledge Representation
Schemes
 Declarative Knowledge- It means representations of
facts and assertions. For ex-college(building, location,
courses etc.)
 Procedural Knowledge- It tells the functioning of the
concerned object.
 Inheritable Knowledge- It inherits the properties of
certain objects.

10.  Inferential Knowledge- It means drawing conclusions
or finding the solution of some problem with the help
of stored knowledge.
 Relational Knowledge- The facts are represented as set
of relations in a tabular form.
 Heuristic Knowledge-It can be defined as an
experimental or the judgmental knowledge of any
performance.

11.  Common Sense Knowledge- It is a general conceptual
knowledge gained by your own experience.
 Explicit Knowledge- It is the one that an individual
holds explicitly and can be expressed in formal
language including mathematical expressions,
grammatical statements etc.
 Tacit Knowledge- It is the knowledge which is
understood but can’t be expressed. This kind of
knowledge is acquired by experience and involves
intangible factors like personal beliefs.

12. Knowledge Representation
 The basic knowledge representation of facts are:
FACTS
INTERNAL
REPRESENTATION
ENGLISH
REPRESENTATION
REASONING
PROGRAM

13.  Some issues in knowledge representation are:
 What are the attributes of objects that should be
captured?
 What relationships does exist among those objects?
 What is the granularity of representation(depth of
detail)?
 What is the inferencing mechanism used?

14. Example of knowledge
Representation
 Production Rule:
Rule consists of condition and an action
IF Part and THEN part.
if(condition) is true, then rule will be followed.

15. Entities of knowledge
representation
 Facts: The entities we want to represent.
 Representation of facts: The facts should be mapped
for the purpose of storage in computer memory. The
representation of facts is very difficult because some of
the entities in real world are not physically visible .

16. Knowledge Acquisition
 It is the gathering of knowledge from various sources
like consulting the experts, collecting from books and
other media.
 It is a continuous process and is spread over entire
lifetime.
 It is the major area of research in building an expert
system.

17. Issues of KA
 Experts have vast amount of knowledge.
 Experts are busy people.
 Experts possess lot of tacit knowledge.
 They do not know all that they know and use.
 Tacit knowledge is difficult to describe.
 Each expert doesn’t know everything.

18. Requirements for KA Techniques
To deal with such issues, techniques are required which:
 All0w non-experts to understand the knowledge.
 Focus on the essential part of the knowledge.
 Can capture Tacit knowledge.
 Allow knowledge to be collected from different
experts.
 Allow knowledge to be manipulated and validated.

19. Knowledge Creation
 From tacit to tacit(Socialization)- sharing experiences
to create tacit knowledge.
 From explicit to tacit(Internalization)- written form of
knowledge into one’s head. It is verbalized into
documents or oral stories to tacit knowledge.
 From tacit to explicit(Externalization)- articulating
knowledge in the head into communicable form
through concepts, hypothesis etc.
 From explicit to explicit(Combination)- transferring
knowledge through documents, meetings and
conversations.

20. KA Techniques
 Protocol-Generation: includes various types of structured
interviews, reporting and observational techniques.
 Protocol-Analysis: analyzing techniques to identify
knowledge goals, decisions, relationships and attributes.
 Hierarchy-Generation: used to build hierarchical
structures such as goal trees decision networks.
 Matrix-Based Techniques: construction of grids indicating
problems for various solutions.
 Sorting Techniques: used for capturing methods by which
people compare and order concepts.
 Diagram-Based Techniques: includes generation and use of
concept maps, event diagrams and process maps.

21. Propositional Calculus
 Propositional calculus (also called propositional logic)
is the branch of mathematical logic concerned with
the study of propositions (whether they are true or
false) that are formed by other propositions with the
use of logical connectives.

22. Examples
 New Delhi is the capital of India.
 The square root of 4 is 2.
 No, thank you.
 Maths is a difficult subject.
 India will be superpower by 2020 AD.

23. Syntax of Propositional calculus
It contains the connectives to combine the propositions.
 Negation( ): not
 Conjunction( ): And
 Disjunction( ): Or
 Implication( ): P implies Q such that if P is true, Q
is true and vice-versa.
 Biconditional( ): If and only If
 Equivalence( ): Equal relational values

24. Semantics of Propositional calculus
 Meaning of the sentences given by the values true or
false.
 Examples are:
 It is not cloudy
 It is not raining
 It is cloudy or it is raining
 It is cloudy and it is raining
 It is cloudy indicates it is raining
(All these are called as Well Formed Formulas)

25. Well Formed Formula
 Generally the propositions, (P and Q) or (Q and P)
provides the same set of values. So to distinguish
between the values, a well formed formula is defined.
 According to WFF,
i. If P is a propositional variable, then it is a WFF.
ii. If P is a WFF, then negation(P) is also a WFF.
iii. If P and Q are WFF, then (P and Q), (P or Q),(P Q) are
WFF too.
iv. A string of symbols is a WFF if and only if it is obtained by a
finite number of applications of (i) – (iii).
 Tautology and Contradiction.

26. Inference Rules in Propositional Logic
 In logical reasoning, certain number of propositions
are assumed to be true and based on that assumption,
some other propositions are derived and it is called as
Inferencing.
 The propositions that are assumed to be true are called
premises.
 The proposition derived by using the rules of inference
is called Conclusion.

27. Rules are:
 Addition: From a given statement P, infer (P OR Q),
where Q can be any other statement.
Example:
Given: Ram is an obedient boy.
Conclusion: Ram is an obedient boy or sushant is a
lazy boy.
Implication Form: P (P OR Q)
 Conjunction: AND
Implication Form: (P AND Q) (P AND Q)
 Simplification: From given sentence (P AND Q),
infer P.

28. Example:
Given: Kate is a beautiful woman and Jane is an Ugly
woman.
Conclusion: Kate is a beautiful woman
Implication Form: (P AND Q) P.
 Modus Ponens: From given two statements P and
(P Q), infer Q.
Example:
Given: Ram is intelligent
and: Ram is intelligent Ram tops the class.
Conclusion: Ram tops the class.
Implication form: (P AND (P Q)) Q.

29.  Modus Tollens: From the given two statements
negation(Q) and (P Q), infer negation(P).
Example:
Given: Ram is not a religious person.
and : Ram goes to church daily implies Ram is a
religious person.
Conclusion: Ram doesn't goes to church daily.
Implication Form: (NOT(Q) AND(P Q)) NOT(P).
 Chain Rule: From (P Q) and (Q R), infer (P R).
Example:
Given: India has natural resources India can generate

30. energy.
and: India can generate energy India is prosperous
country.
Conclusion: India has natural resources India is
prosperous country.
Implication form: ((P Q)AND(Q R)) (P R).
 Disjunctive Syllogism: From two given sentences
negation(P) and (P OR Q), infer Q.
Example:
Given: Mohit is not a laborious boy
and: Mohit is a laborious boy or Suchi is an innocent
girl.

31. Conclusion: Suchi is an honest girl
Implication form: (NOT(P) AND (P OR Q)) Q.
 Constructive dilemma: From given two sentences
((P Q) AND (R S)) and (P OR R), infer (Q OR S).
Implication form:
(((P Q) AND (R S)) AND (P OR R)) (Q OR S).

32. Predicate Logic
 It is another representation technique suited to
represent real world problems.
 Represents the phenomenon by creating symbols
called predicates.
 Predicates are the declarative part of the sentence
describing the properties of any object.
 Example: Ram is a student.
here, predicate is : “is a student”
 FOPL
 First order predicate logic
 It allows quantified variables to refer to objects in the
domain of discourse

33. Symbols for Predicate Calculus
 Set of letters both uppercase and lowercase
 Set of digits 0-9
 The underscore _
 Special characters are not used
 Other than logical connectives, Quantifiers are used.
 Two types of quantifiers:
 known as existential quantifier
 known as universal quantifier

34. INFERENCING IN PREDICATE LOGIC
 Universal Existentiation
 Universal generalization
 Existential Generalization
 Existential Existentiation
 Resolution