Unit 2: Knowledge

Representation
 The importance of knowledge
representation
 Contrary to the beliefs of early workers in AI, experience has shown that
Intelligent Systems cannot achieve anything useful unless they contain a large
amount of real-world - probably domain-specific - knowledge.
 Humans almost always tackle difficult real-world problems by using their
resources of knowledge - "experience", "training" etc.
 This raises the problem of how knowledge can be represented inside a computer,
in such a way that an AI program can manipulate it.
Definition and importance of knowledge
What is knowledge representation?
Humans are best at understanding, reasoning, and interpreting knowledge.
Human knows things, which is knowledge and as per their knowledge they perform various actions in
the real world.
But how machines do all these things comes under knowledge representation and
reasoning.
Hence we can describe Knowledge representation as following:
Knowledge representation and reasoning (KR, KRR) is the part of Artificial intelligence which concerned
with AI agents thinking and how thinking contributes to intelligent behavior of agents.
It is responsible for representing information about the real world so that a computer can understand
and can utilize this knowledge to solve the complex real world problems such as diagnosis a medical
condition or communicating with humans in natural language.
It is also a way which describes how we can represent knowledge in artificial intelligence.
Knowledge representation is not just storing data into some database, but it also enables
an intelligent machine to learn from that knowledge and experiences so that it can behave
intelligently like a human.
What kind of knowledge to
Represent:
 Object: All the facts about objects in our world domain. E.g., Guitars
contains strings, trumpets are brass instruments.
 Events: Events are the actions which occur in our world.
 Performance: It describe behavior which involves knowledge about
how to do things.
 Meta-knowledge: It is knowledge about what we know.
 Facts: Facts are the truths about the real world and what we represent.
 Knowledge-Base: The central component of the knowledge-based
agents is the knowledge base. It is represented as KB. The
Knowledgebase is a group of the Sentences (Here, sentences are used
as a technical term and not identical with the English language).
 Categories of knowledge
 Knowledge can be categorized into two major types:
• Tacit knowledge
• Explicit knowledge
 Tacit knowledge is the knowledge which exists within a
human being. It does correspond to informal or implicit
type of knowledge. It is quite difficult to articulate
formally and is also difficult to communicate and share.
 Explicit knowledge is the knowledge which exists outside
a human being. It corresponds to formal type of
knowledge. It is easier to articulate compared to tacit
knowledge and is easier to share, store or even process.
Types of knowledge
1. Declarative Knowledge:
Declarative knowledge is to know about something.
It includes concepts, facts, and objects.
It is also called descriptive knowledge and expressed in declarative sentences.
It is simpler than procedural language.
2. Procedural Knowledge
It is also known as imperative knowledge.
Procedural knowledge is a type of knowledge which is responsible for knowing how to do something.
It can be directly applied to any task.
It includes rules, strategies, procedures, agendas, etc.
Procedural knowledge depends on the task on which it can be applied.
3. Meta-knowledge:
Knowledge about the other types of knowledge is called Meta-knowledge.
4. Heuristic knowledge:
Heuristic knowledge is representing knowledge of some experts in a field or subject.
Heuristic knowledge is rules of thumb based on previous experiences, awareness of approaches, and which
are good to work but not guaranteed.
5. Structural knowledge:
Structural knowledge is basic knowledge to problem-solving.
It describes relationships between various concepts such as kind of, part of, and grouping of something.
The relation between knowledge and intelligence:

Knowledge: Knowledge is awareness or familiarity gained by experiences of facts, data,

and situations.
Knowledge of real-worlds plays a vital role in intelligence and same for creating artificial
intelligence.
Knowledge plays an important role in demonstrating intelligent behavior in AI agents.
An agent is only able to accurately act on some input when he has some knowledge or
experience about that input.
Let's suppose if you met some person who is speaking in a language which you don't know,
then how you will able to act on that. The same thing applies to the intelligent behavior of
the agents.
 AI knowledge cycle:
An Artificial intelligence system has the following components for
displaying intelligent behavior:
 Perception
 Learning
 Knowledge Representation and Reasoning
 Planning
 Execution
Knowledge Based System

 A knowledge-based system (KBS) is a form of artificial intelligence (AI)

that aims to capture the knowledge of human experts to support
decision-making.
 Example: expert systems -reliance on human expertise.
 architecture of a KBS, has problem-solving method, includes a
knowledge base and an inference engine.
 KBS Architecture

Knowledge module is KB
Control Module is Inference Engine

KBS can be RULE BASED REASONING, MODEL BASED OR CASE BASED

 As the knowledge is represented explicitly in the knowledge
base, rather than implicitly within the structure of a program, it can be
entered and updated with relative ease by domain experts who may not
have any programming expertise.
 A knowledge engineer is someone who provides a bridge between the
domain expertise and the computer implementation. The knowledge
engineer may make use of meta-knowledge, i.e. knowledge about
knowledge, to ensure an efficient implementation.
 Requirements of knowledge Representation
 A knowledge representation has the following requirements
1. It should have the adequacy or fulfillment to represent all types of
knowledge present in the domain. It is also known as representational
adequacy.
2. It should be capable enough to manipulate the representational
structure in order to derive new structures which also should be
corresponding to the new knowledge extracted from the old. It is also
referred as inferential adequacy.
3. It should be able to indulge the additional information into the
knowledge structure which can be further used to focus on inference
mechanisms in the best possible direction. It is sometimes known as
inferential efficiency.
4. It should acquire new knowledge with the help of automatic methods
rather than relying on human source. This process is known as
acquisitional efficiency.
Statement 1: John is a cricketer.
Statement 2: All cricketers are athletes.
Then it can be represented as;
Cricketer(John)
∀x = Cricketer (x) ———-> Athelete (x)
 Schemas
 Knowledge representation often provides information about those things which
occur very common and make a pattern. Such patterned description is known
as schemas. There are various types of schema which are

 Frames– They contain information of all the attributes present in a given

object. As for example, the description of a girl includes hair, facial pattern, eyes,
etc. are considered as the frames. They represent knowledge of concepts and
objects.
 Scripts- They are often used to explain series of events which follow a
sequence. For example, a hotel scene. They represent knowledge of events.
 Stereotypes– They used to describe the characteristics present in a people.
 Rule models- In a production system, they describe features which are shared
commonly among a set of rules.
 Propositional logic
 Propositional logic (PL) is the simplest form of logic where all the statements are made by propositions.
 A proposition is a declarative statement which is either true or false. It is a technique of knowledge
representation in logical and mathematical form.

Facts of Propositional Logic

 Propositional logic is also called Boolean logic as it works on 0 and 1.
 In propositional logic, we use symbolic variables to represent the logic, and we can use any symbol for a
representing a proposition, such A, B, C, P, Q, R, etc.
 Propositions can be either true or false, but it cannot be both.
 Propositional logic consists of an object, relations or function, and logical connectives.
 These connectives are also called logical operators.
 The propositions and connectives are the basic elements of the propositional logic.
 Connectives can be said as a logical operator which connects two sentences.
 A proposition formula which is always true is called tautology, and it is also called a valid sentence.
 A proposition formula which is always false is called Contradiction.
 A proposition formula which has both true and false values is called Contingency
Statements which are questions, commands, or opinions are not propositions such as "Where is Rohini",
"How are you", "What is your name", are not propositions
 Syntax and Semantics

 The syntax of propositional logic defines the allowable sentences for the knowledge
representation. There are two types of Propositions:
1. Atomic Propositions
2. Compound propositions
Atomic Proposition: Atomic propositions are the simple propositions. It consists of a
single proposition symbol. These are the sentences which must be either true or false.
• Example:
• a) 2+2 is 4, it is an atomic proposition as it is a true fact.
• b) "The Sun is cold" is also a proposition as it is a false fact.
Compound proposition: Compound propositions are constructed by combining simpler
or atomic propositions, using parenthesis and logical connectives.
• Example:
• a) "It is raining today, and street is wet."
• b) "Ankit is a doctor, and his clinic is in Mumbai."
 Logical Connectives:

 Logical connectives are used to connect two simpler propositions or representing a sentence
logically. We can create compound propositions with the help of logical connectives.
 There are mainly five connectives, which are given as follows:
1. Negation: A sentence such as ¬ P is called negation of P. A literal can be either Positive literal
or negative literal.
2. Conjunction: A sentence which has ∧ connective such as, P ∧ Q is called a conjunction.
Example: Rohan is intelligent and hardworking. It can be written as,
P= Rohan is intelligent,
Q= Rohan is hardworking. → P∧ Q.
3. Disjunction: A sentence which has ∨ connective, such as P ∨ Q. is called disjunction, where P
and Q are the propositions.
Example: "Ritika is a doctor or Engineer",
Here P= Ritika is Doctor. Q= Ritika is Doctor, so we can write it as P ∨ Q.
4. Implication: A sentence such as P → Q, is called an implication. Implications are also known as
if-then rules. It can be represented as
If it is raining, then the street is wet.
Let P= It is raining, and Q= Street is wet, so it is represented as P → Q
5. Biconditional: A sentence such as P⇔ Q is a Biconditional sentence, example If I am
breathing, then I am alive
P= I am breathing, Q= I am alive, it can be represented as P ⇔ Q.
 Truth Table

 In propositional logic, we need to know the truth values of propositions in all possible
scenarios. We can combine all the possible combination with logical connectives, and
the representation of these combinations in a tabular format is called Truth table.
Following are the truth table for all logical connectives:
Truth table for 3 propositions
Precedence of connectives
 First Precedence Parenthesis
 Second Precedence Negation
 Third Precedence Conjunction(AND)
 Fourth Precedence Disjunction(OR)
 Fifth Precedence Implication
 Six Precedence Biconditional
 Note: For better understanding use parenthesis to make sure of the correct
interpretations. Such as ¬R∨ Q, It can be interpreted as (¬R) ∨ Q.
Logical Equivalence

 Logical equivalence is one of the features of propositional logic. Two

propositions are said to be logically equivalent if and only if the columns
in the truth table are identical to each other.
 Let's take two propositions A and B, so for logical equivalence, we can
write it as A⇔B. In below truth table we can see that column for ¬A∨ B
and A→B, are identical hence A is Equivalent to B
Properties of operators
• Commutativity:
• P∧ Q= Q ∧ P, or
• P ∨ Q = Q ∨ P.
• Associativity:
• (P ∧ Q) ∧ R= P ∧ (Q ∧ R),
• (P ∨ Q) ∨ R= P ∨ (Q ∨ R)
• Identity element:
• P ∧ True = P,
• P ∨ True= True.
• Distributive:
• P∧ (Q ∨ R) = (P ∧ Q) ∨ (P ∧ R).
• P ∨ (Q ∧ R) = (P ∨ Q) ∧ (P ∨ R).
• DE Morgan's Law:
• ¬ (P ∧ Q) = (¬P) ∨ (¬Q)
• ¬ (P ∨ Q) = (¬ P) ∧ (¬Q).
• Double-negation elimination:
Limitations of Propositional logic:
• We cannot represent relations like ALL, some, or none with propositional
logic. Example:
• All the girls are intelligent.
• Some apples are sweet.
• Propositional logic has limited expressive power.
• In propositional logic, we cannot describe statements in terms of their
properties or logical relationships
 First Order Predicate Logic
 Propositional Logic is not sufficient to represent the complex sentences or
natural language statements.
 Some humans are intelligent", or
 "Sachin likes cricket."
 Propositional logic is insufficient to represent these sentences…we need to
have predicate logic..
 First-order logic is also known as Predicate logic or First-order predicate
logic. First-order logic is a powerful language that develops
information about the objects in a more easy way and can also
express the relationship between those objects.
 First-Order Logic(FOL)

 Another way of knowledge representation in artificial intelligence. It is an extension to propositional

logic.
 FOL is sufficiently expressive to represent the natural language statements in a concise way and Also
known as Predicate logic or First-order predicate logic.
 First-order logic is a powerful language that develops information about the objects in a more easy way
and can also express the relationship between those objects.
 First-order logic (like natural language) does not only assume that the world contains facts like
propositional logic but also assumes the following things in the world:
 Objects: A, B, people, numbers, colors, wars, theories, squares, pits, ......
 Relations: It can be unary relation such as: red, round, is adjacent, or n-any relation such as: the sister of,
brother of, has color, comes between
 Function: Father of, best friend, third inning of, end of, ......
 As a natural language, first-order logic also has two main parts:
 Syntax
 Semantics
 Syntax of First-Order logic

 The syntax of FOL determines which collection of symbols is a logical expression in first-order logic. The basic
syntactic elements of first-order logic are symbols. We write statements in short-hand notation in FOL.
Basic Elements of First-order logic:
Following are the basic elements of FOL syntax:
 Constant 1, 2, A, John, Mumbai, cat,.... An individual constant represents a specific object
and is notated a, b, c,….
 Variables .... An individual variable represents any object and notated x, y, z,
….
 Predicates Brother, Father, .... A predicate symbol represents a predicate for
objects and is notated P(x, y), Q(z),…, where P and Q are predicate symbols.
 Function sqrt, .... A functional symbol represents a relation between or among
objects and is notated f(x, y), g(z, w),…. Here the functional symbol g shows the
relationship between z and w
 Connectives ∧, ∨, ¬, ⇒, ⇔A logical symbol represents an operation on predicate
symbols and is notated ↔, ~,→,∨, or ∧
 Equality ==
 Quantifier ∀, ∃
 Predicate sentences

 Atomic sentences
 Complex Sentences

Atomic sentences are the most basic sentences of first-order logic. These sentences are formed from a
predicate symbol followed by a parenthesis with a sequence of terms.
 We can represent atomic sentences as Predicate (term1, term2, ......, term n).
 Example:
Ravi and Ajay are brothers: => Brothers(Ravi, Ajay).
Chinky is a cat => cat (Chinky).

Complex Sentences:
 Complex sentences are made by combining atomic sentences using connectives.
 First-order logic statements can be divided into two parts:

 Subject: Subject is the main part of the statement.

 Predicate: A predicate can be defined as a relation, which binds two atoms together in a statement.
 Consider the statement: "x is an integer.", it consists of two parts, the first part x is the subject of the
statement and second part "is an integer," is known as a predicate.
 QUANTIFIERS IN FOPL
 A quantifier is a language element which generates quantification, and quantification
specifies the quantity of specimen in the universe of discourse.
 These are the symbols that permit to determine or identify the range and scope of the
variable in the logical expression.
 There are two types of quantifier:
 Universal Quantifier, (for all, everyone, everything) (∀)
 Existential quantifier, (for some, at least one) (∃)
 Universal Quantifier:

 Universal quantifier is a symbol of logical representation, which specifies that the statement within its
range is true for everything or every instance of a particular thing.
 The Universal quantifier is represented by a symbol ∀, which resembles an inverted A.
 Note: In universal quantifier we use implication "→".
 If x is a variable, then ∀x is read as:
The main connective for universal quantifier ∀ is implication →
For all x
For each x
For every x.

Ex1. All boys are intelligent

∀ x: boys(x)  intelligent(x)
Ex. All cats are white color
∀x: cats(x) white(x,color)
 All man drink coffee.

 ∀x man(x) → drink (x, coffee).

 Existential quantifier

 Existential quantifiers are the type of quantifiers, which express that the statement within its scope
is true for at least one instance of something.
 It is denoted by the logical operator ∃, which resembles as inverted E. When it is used with a
predicate variable then it is called as an existential quantifier.
 If x is a variable, then ∃ x is read as: •The main connective for existential quantifier ∃ is and ∧.
For some x
For one x
Ex1. Some boys are intelligent
∃x: boys(x) ∧ intelligent(x)
Ex2. Some cats are white color
∃ x: cats(x)^ white(x,color)
 Some boys are intelligent

 ∃x: boys(x) ∧ intelligent(x)

Examples
 Points to remember:
 The main connective for universal quantifier ∀ is implication →.
 The main connective for existential quantifier ∃ is and ∧.
Properties of Quantifiers:
 In universal quantifier, ∀x∀y is similar to ∀y∀x.
 All girls are intelligence or all intelligence are girls
 In Existential quantifier, ∃x∃y is similar to ∃y∃x.
 Some boys are intelligence or some intelligence are boys
 ∃x∀y is not similar to ∀y∃x.
 Someone is loyal to everyone ( not equal) everyone loyal to someone
 Try the examples for FOPL
 All birds fly.
 ∀x: bird(x) →fly(x).
 Every man respects his parent.
 ∀x: man(x) → respects (x, parent).
 Some boys play cricket.
 ∃x: boys(x) → play(x, cricket).
 Not all students like both Mathematics and Science.
¬∀ (x): [ student(x) → like(x, Mathematics) ∧ like(x, Science)].
WFF – Well Formed Formulas
 Properties of Well Formed Formulas

 To translate English into formulas

 Set of strings First find appropriate primitive
propositions:
 1. Every primitive proposition P, Q, R, . . .

is a wff P: Maxi likes cat

2. If A is a wff, so is ¬A Q: Maxi likes dog

3. If A and B are wffs, so are A ∧ B, A ∨ B, Then translate:

A ⇒ B, and A ⇔ B 1. ¬P
2. P ∧ Q
 Example translating English to WFF 3. P ∨ Q
4. P ∧ ¬Q (note: “but” becomes
1.Maxi doesn’t like cat “and”)
2. Maxi likes cat and likes dog 5. P ⇒ ¬Q
3. Maxi likes cat or dog

4. Maxi likes cat but does not like dog

5. If Maxi likes cat then she doesn’t like dog

 REPRESENTING KNOWLEDGE USING
RULES
 The classic methods of representing knowledge use either rules or logic. Table
displays the knowledge for the zoo animals problem in two formats–using rules on
the left as implemented within the Knowledge Representation NetLogo model, and
using first order logic on the right.
 Rules are often used in rule-based expert systems, and are either specified
explicitly by a knowledge engineer (usually through a process called ‘knowledge
acquisition’ from a human expert), or they are derived from data using a machine
learning or data mining algorithm.
 Rules use a logic-based form for reasoning. Logic is the use of symbolic and
mathematical techniques for deductive reasoning, and dates back as a discipline to
Aristotle.
 Reasoning can be Forward or Reverse
 Forward : from the start states towards the goal states
 Reverse: from the goal states to the initial state
Rules can be represented as IF THEN ELSE structure
 REPRESENTING KNOWLEDGE USING RULES

 The rule-based method of knowledge representation uses IF-THEN rules (sometimes called
conditionaction rules) to specify the knowledge.
 All the rules for a particular problem form the rules-base, and the knowledge-base
comprises three components: the list of rules in the rules-base; the list of known facts in
the facts-base; and an inferencing system, which processes the rules to derive new facts
via some form of reasoning.
 A rule consists of an IF part which is a set of conditions (called the antecedents) that must
be met before the rule is said to ‘fire’ so that the set of actions in the THEN part (called
the consequents) are executed.
 For example, for Rule R1 in Table, if the condition ‘animal has hair’ is met–that is, there is a
known fact in the knowledge base that the animal being classified has hair, then the rule is
fired,and the action is to add a further fact ‘species is mammal’ to the knowledge-base.
There may be multiple conditions in the IF part of the rule.For example, Rule R4 has three
conditions that must be met before it can be fired.
 These conditions are separated by the AND keyword, and therefore these conditions are
called ‘conjunctions’. If they were separated by the OR keyword, they would be called
‘disjunctions’.
Representation using Structured
Knowledge
Representing knowledge using logical formalism, like predicate logic, has several advantages. They can be combined with

powerful inference mechanisms like resolution, which makes reasoning with facts easy. But using logical formalism complex

structures of the world, objects and their relationships, events, sequences of events etc. can not be described easily.

A good system for the representation of structured knowledge in a particular domain should posses the following four properties:

(i) Representational Adequacy:- The ability to represent all kinds of knowledge that are needed in that domain.
(ii) Inferential Adequacy :- The ability to manipulate the represented structure and infer new structures.
(iii) Inferential Efficiency:- The ability to incorporate additional information into the knowledge structure that will aid the
inference mechanisms.
(iv) Acquisitional Efficiency :- The ability to acquire new information easily, either by direct insertion or by program control.
Objectives of any AI should be

1. Declarative Methods:- In these knowledge is represented as static

collection of facts which are manipulated by general procedures.
Here the facts need to be stored only one and they can be used in any
number of ways. Facts can be easily added to declarative systems without
changing the general procedures.

2. Procedural Method:- In these knowledge is represented as procedures.

Default reasoning and probabilistic reasoning are examples of
procedural methods. In these, heuristic knowledge of “How to do things
efficiently “can be easily represented.
Schemas

 Knowledge representation often provides information about those things which

occur very common and make a pattern. Such patterned description is known
as schemas. There are various types of schema which are

 Frames– They contain information of all the attributes present in a given

object. As for example, the description of a girl includes hair, facial pattern, eyes,
etc. are considered as the frames. They represent knowledge of concepts and
objects.
 Scripts- They are often used to explain series of events which follow a
sequence. For example, a hotel scene. They represent knowledge of events.
 Stereotypes– They used to describe the characteristics present in a people.
 Rule models- In a production system, they describe features which are shared
commonly among a set of rules.
 Schemas …,

Frames and scripts are used very extensively in a variety of AI programs. Before selecting any
specific knowledge representation structure, the following issues have to be considered.

(i) The basis properties of objects , if any, which are common to every problem domain must
be identified and handled appropriately.

(ii) The entire knowledge should be represented as a good set of primitives.

(iii) Mechanisms must be devised to access relevant parts in a large knowledge base.
 Techniques of knowledge representation

There are mainly four ways of knowledge representation which are given as follows:
 Logical Representation
 Semantic Network Representation
 Frame Representation
 Production Rules
 1. Logical Representation

 Logical representation is a language with some concrete rules which deals with propositions and has no ambiguity in
representation. Logical representation means drawing a conclusion based on various conditions. This representation lays down
some important communication rules. It consists of precisely defined syntax and semantics which supports the sound inference.
Each sentence can be translated into logics using syntax and semantics.
Syntax
 Syntaxes are the rules which decide how we can construct legal sentences in the logic.
 It determines which symbol we can use in knowledge representation.
 How to write those symbols.
Semantics:
 Semantics are the rules by which we can interpret the sentence in the logic.
 Semantic also involves assigning a meaning to each sentence.
 Logical representation can be categorized into mainly two logics:
 Propositional Logics
 Predicate logics
Advantages of logical representation:
 Logical representation enables us to do logical reasoning.
 Logical representation is the basis for the programming languages.
Disadvantages of logical Representation:
 Logical representations have some restrictions and are challenging to work with.
 Logical representation technique may not be very natural, and inference may not be so efficient.
Syntax semantics
• It decides how we can convert • Semantics are the rules which we
legal sentence in logic can interpret the sentence in the
• It determines which symbol we can logic
use in knowledge representation
• it assigns a meaning to each
• Also how to write those symbols sentence.
 2. Semantic Network Representation

 Semantic networks are alternative of predicate logic for knowledge representation. In Semantic networks,
we can represent our knowledge in the form of graphical networks. This network consists of nodes
representing objects and arcs which describe the relationship between those objects. Semantic networks
can categorize the object in different forms and can also link those objects. Semantic networks are easy to
understand and can be easily extended.
This representation consist of mainly two types of relations:
 IS-A relation (Inheritance)
 Kind-of-relation
Example: Following are some statements which we need to represent in the form of nodes and arcs.
Statements:
 Jerry is a cat.
 Jerry is a mammal
 Jerry is owned by Priya.
 Jerry is brown colored.
 All Mammals are animal.
Another example
Drawbacks in Semantic representation:
 Semantic networks take more computational time at runtime as we need to traverse the
complete network tree to answer some questions. It might be possible in the worst case
scenario that after traversing the entire tree, we find that the solution does not exist in this
network.
 Semantic networks try to model human-like memory (Which has 1015 neurons and links) to
store the information, but in practice, it is not possible to build such a vast semantic network.
 These types of representations are inadequate as they do not have any equivalent quantifier,
e.g., for all, for some, none, etc.
 Semantic networks do not have any standard definition for the link names.
 These networks are not intelligent and depend on the creator of the system.
Advantages of Semantic network:
 Semantic networks are a natural representation of knowledge.
 Semantic networks convey meaning in a transparent manner.
 These networks are simple and easily understandable.
 3. Frame Representation

 A frame is a record like structure which consists of a collection of attributes and its values to
describe an entity in the world. Frames are the AI data structure which divides knowledge into
substructures by representing stereotypes situations. It consists of a collection of slots and slot
values. These slots may be of any type and sizes. Slots have names and values which are called
facets.
 Facets: The various aspects of a slot is known as Facets. Facets are features of frames which
enable us to put constraints on the frames. Example: IF-NEEDED facts are called when data of
any particular slot is needed. A frame may consist of any number of slots, and a slot may include
any number of facets and facets may have any number of values. A frame is also known as slot-
filter knowledge representation in artificial intelligence.
 Frames are derived from semantic networks and later evolved into our modern-day classes and
objects. A single frame is not much useful. Frames system consist of a collection of frames which
are connected. In the frame, knowledge about an object or event can be stored together in the
knowledge base. The frame is a type of technology which is widely used in various applications
including Natural language processing and machine visions.
 Example 2

Let's suppose we are taking an entity, Peter. Peter is an engineer as a profession, and his age is 25, he
lives in city London, and the country is England. So following is the frame representation for this:

Slots Filter

Name Peter
Profession Doctor
Age 25
Marital status Single
Weight 78
Example: 1

 Let's take an example of a frame for a book

 Slots Filters
 Title Artificial Intelligence
 Genre Computer Science
 Author Peter Norvig
 Edition Third Edition
 Year 1996
 Page 1152
Frame Representation…..

Advantages of frame representation:

 The frame knowledge representation makes the programming easier by grouping the related data.
 The frame representation is comparably flexible and used by many applications in AI.
 It is very easy to add slots for new attribute and relations.
 It is easy to include default data and to search for missing values.
 Frame representation is easy to understand and visualize.
Disadvantages of frame representation:
 In frame system inference mechanism is not be easily processed.
 Inference mechanism cannot be smoothly proceeded by frame representation.
 Frame representation has a much generalized approach.
 4. Production Rules

Production rules system consist of (condition, action) pairs which mean, "If condition then action". It has
mainly three parts:
 The set of production rules
 Working Memory
 The recognize-act-cycle
 In production rules agent checks for the condition and if the condition exists then production rule fires and
corresponding action is carried out. The condition part of the rule determines which rule may be applied to
a problem. And the action part carries out the associated problem-solving steps. This complete process is
called a recognize-act cycle.
 The working memory contains the description of the current state of problems-solving and rule can write
knowledge to the working memory. This knowledge match and may fire other rules.
 If there is a new situation (state) generates, then multiple production rules will be fired together, this is
called conflict set. In this situation, the agent needs to select a rule from these sets, and it is called a conflict
resolution.
Example:
 IF (at bus stop AND bus arrives) THEN action (get into the bus)
 IF (on the bus AND paid AND empty seat) THEN action (sit down).
 IF (on bus AND unpaid) THEN action (pay charges).
 IF (bus arrives at destination) THEN action (get down from the bus).
Advantages of Production rule:
 The production rules are expressed in natural language.
 The production rules are highly modular, so we can easily remove, add or modify an individual rule.
Disadvantages of Production rule:
 Production rule system does not exhibit any learning capabilities, as it does not store the result of the
problem for the future uses.
 During the execution of the program, many rules may be active hence rule-based production systems
are inefficient.
 Associative network
 An associate memory network refers to a content addressable memory
structure that associates a relationship between the set of input
patterns and output patterns. A content addressable memory structure
is a kind of memory structure that enables the recollection of data based
on the intensity of similarity between the input pattern and the patterns
stored in the memory.
 Let's understand this concept with an example:
The figure given below illustrates a memory
containing the names of various people. If
the given memory is content addressable,
the incorrect string "Albert Einstein" as a key
is sufficient to recover the correct name
"Albert Einstein."

In this condition, this type of memory is

robust and fault-tolerant because of this type
of memory model, and some form of error-
correction capability.
Conceptual dependency

 One of the key ideas of the script approach is to reduce a sentence or

story to a set of semantic primitives using a formalism called conceptual
dependency (CD).
 Conceptual dependency (CD) is a theory of natural language processing
which mainly deals with representation of semantics of a language.
 Conceptual dependency theory is based on the use of knowledge
representation methodology was primarily developed to understand
and represent natural language structures.
6 primitive conceptual categories
a. ACTS : Actions
b. PPs : Objects (Picture
Producers)
c. AAs : Modifiers of Actions
(Action Aiders)
d. Pas : Modifiers of PPs (Picture
Aiders)
e. TS : Time of action
f. Loc : Location
THANK YOU..