This document provides an introduction to knowledge representation in artificial intelligence. It discusses how knowledge representation and reasoning forms the basis of intelligent behavior through computational means. The key types of knowledge that need to be represented are defined, including objects, events, facts, and meta-knowledge. Different types of knowledge such as declarative, procedural, structural and heuristic knowledge are explained. The importance of knowledge representation for modeling intelligent behavior in agents is highlighted. The requirements for effective knowledge representation including representational adequacy, inferential adequacy, inferential efficiency, and acquisitional efficiency are outlined. Propositional logic is introduced as the simplest form of logic using propositions.

1. Unit 2: Knowledge
Representation
Dr. G.Jasmine Beulah,
Assistant Professor, Dept. Computer Science,
Kristu Jayanti College, Bengaluru

2. Introduction
 Knowledge Representation and Reasoning forms the backbone of any
Intelligent Behavior through Computational means.
 Human Intelligence is also driven through Knowledge.
Human beings are good at understanding, reasoning and interpreting
knowledge.
And using this knowledge, they are able to perform various actions in
the real world.
But how do machines perform the same?

3. Intelligent Behavior: Human Vs Artificial
• Human Intelligence
 Most Complex and Mysterious phenomenon
 Striking aspect of Intelligent behavior is that it is clearly conditioned
by knowledge.
Decisions for a wide range of activities are based on what we know
(or beliefs).

4. Intelligent Behavior: Human Vs Artificial
• Intelligent Behavior through Computational means:
Knowledge Representation and Reasoning is concerned with how an
agent uses what it knows in deciding what to do
 Structures for representing the knowledge
 Computational Processes for reasoning with those structures.

7. Definition and Importance of Knowledge
Knowledge Representation in AI describes the representation of
knowledge.
Basically, it is a study of how the beliefs, intentions,
and judgments of an intelligent agent can be expressed suitably for
automated reasoning.
One of the primary purposes of Knowledge Representation includes
modeling intelligent behavior for an agent.

8. Definition and Importance of Knowledge
Knowledge Representation and Reasoning (KR, KRR) represents
information from the real world for a computer to understand and then
utilize this knowledge to solve complex real-life problems like
communicating with human beings in natural language.
Knowledge representation in AI is not just about storing data in a
database, it allows a machine to learn from that knowledge and behave
intelligently like a human being.

9. What kind of knowledge to Represent:
The different kinds of knowledge that need to be represented in AI include:
Objects - 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
Facts - Facts are the truths about the real world and what we represent
Meta-Knowledge It is knowledge about what we know.
Knowledge-base - - The central component of the knowledge-based agents is the
knowledge base. It is represented as KB. The Knowledge base is a group of the
Sentences (Here, sentences are used as a technical term and not identical with the
English language).

10. What kind of knowledge to Represent:
 How to represent Knowledge?
 How to implement the process of Reasoning?

12. Different Types of Knowledge

13. 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 filed 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.
It describes the relationship that exists between concepts or objects
Eg: “ red” represents color red
“car1” represents my car
red(car1) represents the fact that my car is red.

14. Relation between Knowledge & Intelligence?
 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.
 In the diagram, there is one decision maker which act by
sensing the environment and using knowledge. But if the
knowledge part will not present then, it cannot display
intelligent behavior.

15. Cycle of Knowledge Representation in AI
Artificial Intelligent Systems usually consist of various components to
display their intelligent behavior. Some of these components include:
Perception
Learning
Knowledge Representation & Reasoning
Planning
Execution

16. Example

17. Perception component
The Perception component retrieves data or information from
the environment. with the help of this component, you can
retrieve data from the environment
Find out the source of noises and check if the AI was damaged
by anything.
 Also, it defines how to respond when any sense has been
detected.

18. Learning Component
There is the Learning Component that learns from the captured data
by the perception component.
The goal is to build computers that can be taught instead of
programming them.
Learning focuses on the process of self-improvement.
In order to learn new things, the system requires knowledge
acquisition, inference, acquisition of heuristics, faster searches, etc.

19. Main Component
The main component in the cycle is Knowledge Representation and
Reasoning which shows the human-like intelligence in the machines.
Knowledge representation is all about understanding intelligence.
Instead of trying to understand or build brains from the bottom up, its
goal is to understand and build intelligent behavior from the top-down
and focus on what an agent needs to know in order to behave
intelligently.
Also, it defines how automated reasoning procedures can make this
knowledge available as needed.

20. Planning and Execution components
The Planning and Execution components depend on the analysis of
knowledge representation and reasoning.
Here, planning includes giving an initial state, finding their
preconditions and effects, and a sequence of actions to achieve a state
in which a particular goal holds.
 Now once the planning is completed, the final stage is the execution
of the entire process.

21. Knowledge Based System
• A knowledge-based system (KBS) is a program that captures and uses
knowledge from a variety of sources.
• A KBS assists with solving problems, particularly complex issues, by
artificial intelligence.
• These systems are primarily used to support human decision making,
learning, and other activities.
• A knowledge-based system is a major area of artificial intelligence.
• These systems can make decisions based on the data and information
that resides in their database.
• In addition, they can comprehend the context of the data being
processed.

22. • A knowledge-based system is comprised of a knowledge base and an
interface engine.
• The knowledge base functions as the knowledge repository, while the
interface engine functions as the search engine.
• Learning is a key element to a knowledge-based system, and learning
simulation improves the system over time.
• Knowledge-based systems are categorized as expert systems,
intelligent tutoring systems, hypertext manipulations systems, CASE-
based systems, and databases having an intelligent user interface.

23. • Knowledge-based systems work across a number of applications. For
instance, in the medical field, a KBS can help doctors more accurately
diagnose diseases.
• These systems are called clinical decision-support systems in the
health industry.
• A KBS can also be used in areas as diverse as industrial equipment
fault diagnosis, avalanche path analysis, and cash management.

24. KBS Architecture
KBS can be RULE BASED REASONING, MODEL BASED OR CASE BASED REASONING
Knowledge module is KB
Control Module is Inference Engine

25. 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.

26. 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.

31. 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.

32. Following are some basic facts about 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.
• 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.

33. Syntax of propositional logic:
• The syntax of propositional logic defines the allowable sentences for
the knowledge representation. There are two types of Propositions:
• Atomic Propositions
• 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.

34. • Compound proposition: Compound propositions are constructed by
combining simpler or atomic propositions, using parenthesis and
logical connectives.

35. 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:
• Negation: A sentence such as ¬ P is called negation of P. A literal can
be either Positive literal or negative literal.
• 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.

36. Logical Connectives:
• 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.
• 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
• 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

37. Thank You