Architecture of Expert System in Artificial Intelligence

Architecture of Expert System:

An expert system is an AI program that uses knowledge to solve problems that would normally require a human expert. It attempts to reproduce the performance of one or more human experts. Both factual and heuristic knowledge requires a complex decision. Knowledge engineers design various components which work together to perform different functions. In Artificial Intelligence, There are three main components in the architecture of an expert system.

Knowledge Base: It consists of problem-solving rules, procedures, and intrinsic data relevant to the problem domain.

Working Memory: It refers to task-specific data for the problem under consideration.

Inference Engine: It is a generic control mechanism. It also applies the axiomatic knowledge in the KB to the task-specific data to arrive at some solution or conclusion.

Note: The KB is the central nucleus of the Expert system. KB is different from a database as a traditional database environment. It deals with the data that have a static relationship between the elements in the problem domain. A KB has been created by knowledge engineers who translate the knowledge of real human experts into rules and strategies.

Components of Expert System:

There are three various components in Expert System –

i. Working Memory: The contents of the working memory change with each problem. So, it is the most dynamic component of an expert system. It also assumes that is kept current.

ii. Knowledge Base: A KB changes only if some new information arises that indicates a change in the problem-solving procedure. The Changes in KB should be carefully evaluated before being implemented. The KB of an expert system contains both behavioural and procedural knowledge. The procedural knowledge is rule-based.

iii. Inference Engine: The changes made to the inference engine only if required to make inferential engines are changed only if the developer wishes to.