Knowledge Processing and Knowledge-based Systems

- Overview

In the field of artificial intelligence (AI), whether in the field of machine learning (ML) or deep learning (DL), many complex tasks need to be evaluated. There is indeed a need to automate knowledge processing systems in such systems. 

Knowledge representation is such a process that depends on logical situations and enables policies to make decisions when knowledge is acquired. Human beings acquire various types and levels of knowledge in daily life, but it is difficult for machines to explain all types of knowledge. For this case, a knowledge representation is used. 

In knowledge representation algorithms, AI agents tend to think and participate in decision-making. With this complex thinking, they are able to solve complex problems in real-world scenarios that are difficult and time-consuming for humans.

- Knowledge-based Systems (KBS)

Knowledge processing is the process of organizing information about human knowledge, experience, and sensations. This allows machines to understand people, or people to understand machines. 

Knowledge-based systems (KBS) can help with expert decision-making, especially when a human expert is not available. KBS systems can also: 

• Provide efficient documentation for users to access quickly
• Create new knowledge by referring to and reviewing existing stored data
• Support decision-making and problem-solving

The architecture of a KBS is its knowledge base and inference engine. The knowledge base holds a collection of data, and the inference engine can deduce insights from the data. 

KBS systems differ from machine learning (ML) systems in that KBS systems never find a model of the problem. Instead, they infer by using simple facts and if-then rules. ML systems, on the other hand, form a latent model of the problem.

[More to come ...]