Describe components and structure of expert systems
🤖 Expert Systems – Components and Structure
An expert system is a computer program that mimics the decision‑making ability of a human expert. It combines specialised knowledge with reasoning techniques to solve problems in a specific domain.
🧠 Knowledge Base
The knowledge base stores facts and rules about the problem domain. Knowledge is usually expressed as IF‑THEN rules or frames.
⚙️ Inference Engine
The inference engine applies logical rules to the knowledge base to deduce new information or make decisions. It uses forward chaining (data‑driven) or backward chaining (goal‑driven) reasoning.
💬 User Interface
The user interface allows the user to interact with the system – entering data, asking questions, and receiving explanations. It can be text‑based, graphical, or voice‑enabled.
📖 Explanation Facility
The explanation facility shows how the system reached a conclusion, displaying the chain of rules used. This helps users trust and understand the advice.
🔧 Knowledge Acquisition Subsystem
The knowledge acquisition subsystem helps experts encode their knowledge into the system, often through interviews, questionnaires, or machine‑learning techniques.
📊 Summary Table
| Component | Function |
|---|---|
| Knowledge Base | Stores domain facts and rules (IF‑THEN, frames). |
| Inference Engine | Applies reasoning (forward/backward chaining) to derive conclusions. |
| User Interface | Enables interaction: input, queries, and output display. |
| Explanation Facility | Shows the reasoning trail behind a recommendation. |
| Knowledge Acquisition Subsystem | Assists experts in entering and updating knowledge. |
💡 Tip: Think of an expert system as a digital advisor – the knowledge base is its library, the inference engine is its librarian, and the user interface is the help desk you talk to.
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