Knowledge Representation Approaches in Artificial intelligence

 Knowledge Representation 

The area of artificial intelligence that deals with knowledge representation and reasoning is what helps AI agents think and how thinking impacts their intelligent behavior. 

Its job is to express real-world information in a way that computers can understand and apply to solve complicated real-world issues, such as diagnosing a medical condition or having natural language conversations with people.

It also explains how knowledge can be represented in artificial intelligence. Knowledge representation allows an intelligent computer to learn from its experiences and knowledge to behave intelligently, much like a person. It goes above simply putting data into a database.

Knowledge  Representation Approaches in Artificial Intelligence

The four primary methods for representing knowledge are listed below: 

1. Simple Relational Knowledge

This is the most basic form of relational fact storage, where each fact about a group of objects is arranged methodically in columns. 

Database systems that represent the relationships between various entities are well-known for using this method of knowledge representation. 

There is not much room for interpretation with this method.

An example of a basic relational knowledge representation is given below.

knowledge-representation-approaches-in-artificial-intelligence, Knowledge Representation Approaches in Artificial intelligence

2. Inheritable Knowledge

All data must be kept in a hierarchy of classes according to the inheritable knowledge method. 

Every class should be set up either hierarchically or in a generalized way. 

This method makes use of inheritance property. Values are passed down from other class members to elements. 

This method includes inheritable knowledge known as instance relation, which illustrates the relationship between instance and class. 

A frame-by-frame representation is possible for the set of attributes and their value. 

This method uses boxed nodes to represent values and objects. Arrows, which point from objects to their values, are what we use.

For example:


3. Inferential knowledge: 

The approach to inferential knowledge uses formal logic to represent knowledge. More facts can be obtained by using this method. 

It promised accuracy. 

As an illustration, let's say there are two statements:

a. Marcus is a male. 

b. Every man is mortal. Afterward,

it could appear as; 

Marcus's man ∂x = Man (x) mortal (x)s ---------->

4. Procedural knowledge: 

This method makes use of little programs and codes that specify how to carry out particular tasks and move forward. 

The If-Then rule is one of the key rules in this strategy. We may employ a variety of coding languages, including LISP and Prolog, with this understanding. 

This allows us to represent domain-specific or heuristic knowledge with ease. However, we are not required to be able to represent every scenario using this method.

Post a Comment

* Please Don't Spam Here. All the Comments are Reviewed by Admin.

Top Post Ad

Below Post Ad

Ads Section