Expert systems are a subset of artificial intelligence technology.
These systems mimic the decision-making capabilities of a human expert in a specific domain.
Architecture of expert systems is divided into three main components.
1. Knowledge Base:
This is where all the domain-specific information is stored in the form of rules, facts, and heuristics.
2. Inference Engine:
This component is responsible for applying the knowledge stored in the knowledge base to make decisions and solve problems.
3. User Interface:
The user interface allows users to interact with the expert system and input information or receive advice.
Expert systems rely on algorithms and logic to process information and generate responses.
These systems can be rule-based, case-based, or model-based depending on the approach used to represent knowledge.
Rule-based expert systems use a set of if-then rules to make decisions.
Case-based expert systems compare new cases with previously solved cases to provide solutions.
Model-based expert systems use mathematical models to predict outcomes based on input data.
Expert systems have been used in various industries such as healthcare, finance, and manufacturing to automate decision-making processes.
Demystifying expert system architecture helps to understand how AI technology is applied in real-world scenarios.