An expert system is a software system that attempts to reproduce the performance of one or more human experts, most commonly in a specific problem domain, and is a traditional application and/or subfield of artificial intelligence.
A wide variety of methods can be used to simulate the performance of the expert however common to most or all are :
- the creation of a so-called “knowledge base” which uses some knowledge representation formalism to capture the subject matter experts (SME) knowledge.
- a process of gathering that knowledge from the SME and codifying it according to the formalism, which is called knowledge engineering. Expert systems may or may not have learning components but a third common element is that once the system is developed it is proven by being placed in the same real world problem solving situation as the human SME, typically as an aid to human workers or a supplement to some information system.
Advantages and Disadvantages of ES
- Provides consistent answers for repetitive decisions, processes and tasks.
- Holds and maintains significant levels of information.
- Encourages organizations to clarify the logic of their decision-making
- Never “forgets” to ask a question, as a human might.
- Lacks common sense needed in some decision making.
- Cannot make creative responses as human expert would in unusual
- Domain experts not always able to explain their logic and reasoning.
- Errors may occur in the knowledge base, and lead to wrong decisions.
- Cannot adapt to changing environments, unless knowledge base is changed.