THE FEDERAL POLYTECHNIC NASARAWA

SCHOOL INFORMATION TECHNOLOGY

BY

MAHMUD ASHIRU ABUBAKAR. 1840

SUBMITTED
TO
MALL RIDWAN KAMALUDEEN

JANUARY, 2024

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Introduction

What is a knowledge representation? We argue that the notion can best be

understood in terms of five distinct roles it plays, each crucial to the task at hand:

 A knowledge representation (KR) is most fundamentally a surrogate, a

substitute for the thing itself, used to enable an entity to determine

consequences by thinking rather than acting, i.e., by reasoning about the

world rather than taking action in it.

 It is a set of ontological commitments, i.e., an answer to the question: In

what terms should I think about the world?

 It is a fragmentary theory of intelligent reasoning, expressed in terms of

three components: (i) the representation's fundamental conception of

intelligent reasoning; (ii) the set of inferences the representation sanctions;

and (iii) the set of inferences it recommends.

 It is a medium for pragmatically efficient computation, i.e., the

computational environment in which thinking is accomplished. One

contribution to this pragmatic efficiency is supplied by the guidance a

representation provides for organizing information so as to facilitate making

the recommended inferences.

 It is a medium of human expression, i.e., a language in which we say things

about the world.

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Understanding the roles and acknowledging their diversity has several useful

consequences. First, each role requires something slightly different from a

representation; each accordingly leads to an interesting and different set of

properties we want a representation to have.

Second, we believe the roles provide a framework useful for characterizing a wide

variety of representations. We suggest that the fundamental "mindset" of a

representation can be captured by understanding how it views each of the roles,

and that doing so reveals essential similarities and differences.

Third, we believe that some previous disagreements about representation are

usefully disentangled when all five roles are given appropriate consideration. We

demonstrate this by revisiting and dissecting the early arguments concerning

frames and logic.

Knowledge Representation and Intelligence or Knowledge Representation and

Reasoning (KRI and KRR) is the field of artificial intelligence (AI) dedicated to

representing information about the world in a form that a computer system can use

to solve complex tasks such as diagnosing a medical condition or having a dialog

in a natural language. Knowledge representation incorporates findings from

psychology about how humans solve problems, and represent knowledge in order

to design formalisms that will make complex systems easier to design and build.

Knowledge representation and reasoning also incorporates findings from logic to

automate various kinds of reasoning.

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Examples of knowledge representation formalisms include semantic

nets, frames, rules, logic programs and ontologies. Examples of automated

reasoning engines include inference engines, theorem provers, model

generators and classifiers.

What to Represent:

Following are the kind of knowledge which needs to be represented in AI systems:

o Object: All the facts about objects in our world domain. E.g., Guitars

contains strings, trumpets are brass instruments.

o Events: Events are the actions which occur in our world.

o Performance: It describe behavior which involves knowledge about how to

do things.

o Meta-knowledge: It is knowledge about what we know.

o Facts: Facts are the truths about the real world and what we represent.

o 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).

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

data, and situations. Following are the types of knowledge in artificial intelligence:

Types of knowledge

Following are the various types of knowledge:

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1. Declarative Knowledge:

o Declarative knowledge is to know about something.

o It includes concepts, facts, and objects.

o It is also called descriptive knowledge and expressed in

declarativesentences.

o It is simpler than procedural language.

2. Procedural Knowledge

o It is also known as imperative knowledge.

o Procedural knowledge is a type of knowledge which is responsible for

knowing how to do something.

o It can be directly applied to any task.

o It includes rules, strategies, procedures, agendas, etc.

o Procedural knowledge depends on the task on which it can be applied.

3. Meta-knowledge:

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 o Knowledge about the other types of knowledge is called Meta-knowledge.

4. Heuristic knowledge:

o Heuristic knowledge is representing knowledge of some experts in a filed or

subject.

o 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:

o Structural knowledge is basic knowledge to problem-solving.

o It describes relationships between various concepts such as kind of, part of,

and grouping of something.

o It describes the relationship that exists between concepts or objects.

The relation between knowledge and 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.

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As we can see in below 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.

AI knowledge cycle:

An Artificial intelligence system has the following components for displaying

intelligent behavior:

o Perception

o Learning

o Knowledge Representation and Reasoning

o Planning

o Execution

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The above diagram is showing how an AI system can interact with the real world

and what components help it to show intelligence. AI system has Perception

component by which it retrieves information from its environment. It can be visual,

audio or another form of sensory input. The learning component is responsible for

learning from data captured by Perception comportment. In the complete cycle, the

main components are knowledge representation and Reasoning. These two

components are involved in showing the intelligence in machine-like humans.

These two components are independent with each other but also coupled together.

The planning and execution depend on analysis of Knowledge representation and

reasoning.

Approaches to knowledge representation:

There are mainly four approaches to knowledge representation, which are

givenbelow:

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1. Simple relational knowledge:

o It is the simplest way of storing facts which uses the relational method, and

each fact about a set of the object is set out systematically in columns.

o This approach of knowledge representation is famous in database systems

where the relationship between different entities is represented.

o This approach has little opportunity for inference.

2. Inheritable knowledge:

o In the inheritable knowledge approach, all data must be stored into a

hierarchy of classes.

o All classes should be arranged in a generalized form or a hierarchal manner.

o In this approach, we apply inheritance property.

o Elements inherit values from other members of a class.

o This approach contains inheritable knowledge which shows a relation

between instance and class, and it is called instance relation.

o Every individual frame can represent the collection of attributes and its

value.

o In this approach, objects and values are represented in Boxed nodes.

o We use Arrows which point from objects to their values.

o Example:

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3. Inferential knowledge:

o Inferential knowledge approach represents knowledge in the form of formal

logics.

o This approach can be used to derive more facts.

o It guaranteed correctness.

4. Procedural knowledge:

o Procedural knowledge approach uses small programs and codes which

describes how to do specific things, and how to proceed.

o In this approach, one important rule is used which is If-Then rule.

o In this knowledge, we can use various coding languages such as LISP

language and Prolog language.

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 o We can easily represent heuristic or domain-specific knowledge using this

approach.

o But it is not necessary that we can represent all cases in this approach.

Requirements for knowledge Representation system:

A good knowledge representation system must possess the following properties.

1. Representational Accuracy: KR system should have the ability to

represent all kind of required knowledge.

2. Inferential Adequacy: KR system should have ability to manipulate the

representational structures to produce new knowledge corresponding to

existing structure.

3. Inferential Efficiency: The ability to direct the inferential knowledge

mechanism into the most productive directions by storing appropriate

guides.

4. Acquisitional efficiency- The ability to acquire the new knowledge easily

using automatic methods.

Characteristic

In 1985, Ron Brachman categorized the core issues for knowledge representation

as follows:

 Primitives: What is the underlying framework used to represent

knowledge? Semantic networks were one of the first knowledge representation

primitives. Also, data structures and algorithms for general fast search. In this

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 area, there is a strong overlap with research in data structures and algorithms in

computer science. In early systems, the Lisp programming language, which was

modeled after the lambda calculus, was often used as a form of functional

knowledge representation. Frames and Rules were the next kind of primitive.

 Meta-representation: This is also known as the issue of reflection in computer

science. It refers to the capability of a formalism to have access to information

about its own state. An example would be the meta-object protocol

in Smalltalk and CLOS that gives developers run time access to the class

objects and enables them to dynamically redefine the structure of the

knowledge base even at run time. Meta-representation means the knowledge

representation language is itself expressed in that language.

 Incompleteness: Traditional logic requires additional axioms and constraints to

deal with the real world as opposed to the world of mathematics. Also, it is

often useful to associate degrees of confidence with a statement. I.e., not simply

say "Socrates is Human" but rather "Socrates is Human with confidence 50%".

This was one of the early innovations from expert systems research which

migrated to some commercial tools, the ability to associate certainty factors

with rules and conclusions. Later research in this area is known as fuzzy logic.

 Definitions and universals vs. facts and defaults: Universals are general

statements about the world such as "All humans are mortal". Facts are specific

examples of universals such as "Socrates is a human and therefore mortal". In

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 logical terms definitions and universals are about universal quantification while

facts and defaults are about existential quantifications. All forms of knowledge

representation must deal with this aspect and most do so with some variant of

set theory, modeling universals as sets and subsets and definitions as elements

in those sets.

 Non-monotonic reasoning: Non-monotonic reasoning allows various kinds of

hypothetical reasoning. The system associates facts asserted with the rules and

facts used to justify them and as those facts change updates the dependent

knowledge as well. In rule based systems this capability is known as a truth

maintenance system.

 Expressive adequacy: The standard that Brachman and most AI researchers

use to measure expressive adequacy is usually First Order Logic (FOL).

Theoretical limitations mean that a full implementation of FOL is not practical.

Researchers should be clear about how expressive (how much of full FOL

expressive power) they intend their representation to be.

 Reasoning efficiency: This refers to the run time efficiency of the system. The

ability of the knowledge base to be updated and the reasoner to develop new

inferences in a reasonable period of time. In some ways, this is the flip side of

expressive adequacy. In general, the more powerful a representation, the more

it has expressive adequacy, the less efficient its automated reasoning engine

will be. Efficiency was often an issue,

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REFERENCE

 Ronald J. Brachman; What IS-A is and isn't. An Analysis of Taxonomic Links

in Semantic Networks; IEEE Computer, 16 (10); October 1983

 Ronald J. Brachman, Hector J. Levesque Knowledge Representation and

Reasoning, Morgan Kaufmann, 2004 ISBN 978-1-55860-932-7

 Ronald J. Brachman, Hector J. Levesque (eds) Readings in Knowledge

Representation, Morgan Kaufmann, 1985, ISBN 0-934613-01-X

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Computational Foundations of Conceptual Graphs, Springer, 2009,ISBN 978-

1-84800-285-2.

 Randall Davis, Howard Shrobe, and Peter Szolovits; What Is a Knowledge

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 Ronald Fagin, Joseph Y. Halpern, Yoram Moses, Moshe Y. Vardi Reasoning

About Knowledge, MIT Press, 1995, ISBN 0-262-06162-7

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 Jean-Luc Hainaut, Jean-Marc Hick, Vincent Englebert, Jean Henrard, Didier

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 Arthur B. Markman: Knowledge Representation Lawrence Erlbaum Associates,

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 John F. Sowa: Knowledge Representation: Logical, Philosophical, and

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 Adrian Walker, Michael McCord, John F. Sowa, and Walter G.

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