Data Warehousing
Top 25 ETL Testing Interview Questions & Answers
Following are frequently asked questions in interviews for freshers as well experienced ETL tester and...
Expert System is an interactive and reliable computer-based decision-making system which uses both facts and heuristics to solve complex decision-making problems. It is considered at the highest level of human intelligence and expertise. The purpose of an expert system is to solve the most complex issues in a specific domain.
The Expert System in AI can resolve many issues which generally would require a human expert. It is based on knowledge acquired from an expert. Artificial Intelligence and Expert Systems are capable of expressing and reasoning about some domain of knowledge. Expert systems were the predecessor of the current day artificial intelligence, deep learning and machine learning systems.
In this tutorial, you will learn:
Following are the Expert System Examples:
Following are the important Characteristics of Expert System in AI:
The Expert System in AI consists of the following given components:
The user interface is the most crucial part of the Expert System Software. This component takes the user's query in a readable form and passes it to the inference engine. After that, it displays the results to the user. In other words, it's an interface that helps the user communicate with the expert system.
The inference engine is the brain of the expert system. Inference engine contains rules to solve a specific problem. It refers the knowledge from the Knowledge Base. It selects facts and rules to apply when trying to answer the user's query. It provides reasoning about the information in the knowledge base. It also helps in deducting the problem to find the solution. This component is also helpful for formulating conclusions.
The knowledge base is a repository of facts. It stores all the knowledge about the problem domain. It is like a large container of knowledge which is obtained from different experts of a specific field.
Thus we can say that the success of the Expert System Software mainly depends on the highly accurate and precise knowledge.
Facts and Rules
A fact is a small portion of important information. Facts on their own are of very limited use. The rules are essential to select and apply facts to a user problem.
Knowledge Acquisition
The term knowledge acquisition means how to get required domain knowledge by the expert system. The entire process starts by extracting knowledge from a human expert, converting the acquired knowledge into rules and injecting the developed rules into the knowledge base.
Participant | Role |
Domain Expert | He is a person or group whose expertise and knowledge is taken to develop an expert system. |
Knowledge Engineer | Knowledge engineer is a technical person who integrates knowledge into computer systems. |
End User | It is a person or group of people who are using the expert system to get to get advice which will not be provided by the expert. |
Conventional System | Expert System |
Knowledge and processing are combined in one unit. | Knowledge database and the processing mechanism are two separate components. |
The programme does not make errors (Unless error in programming). | The Expert System may make a mistake. |
The system is operational only when fully developed. | The expert system is optimized on an ongoing basis and can be launched with a small number of rules. |
Step by step execution according to fixed algorithms is required. | Execution is done logically & heuristically. |
It needs full information. | It can be functional with sufficient or insufficient information. |
Human Expert | Artificial Expertise |
Perishable | Permanent |
Difficult to Transfer | Transferable |
Difficult to Document | Easy to Document |
Unpredictable | Consistent |
Expensive | Cost effective System |
Some popular Application of Expert System:
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