Know and understand how an expert system is used to produce possible solutions for different scenarios.
1. What is an Expert System?
An expert system is a type of artificial intelligence (AI) software that mimics the decision‑making ability of a human expert. It captures specialised knowledge in a particular domain and applies logical reasoning to suggest solutions or recommendations.
2. Main Components
Component
Purpose
Typical Example
Knowledge Base
Stores facts, rules, heuristics and relationships of the domain.
Medical symptoms → disease rules.
Inference Engine
Applies logical reasoning to the knowledge base to draw conclusions.
Forward chaining, backward chaining algorithms.
User Interface
Allows users to input data and receive explanations of the system’s reasoning.
Question‑answer dialogue box.
Explanation Facility
Provides a trace of how a conclusion was reached.
“Because symptom X and Y match disease Z.”
Knowledge Acquisition Module
Helps experts input or update knowledge.
Rule editor used by a doctor.
3. How Does an Expert System Work?
The system follows a cycle of input → reasoning → output.
User Input: Data about the problem is entered (e.g., symptoms, error messages).
Reasoning: The inference engine matches the input with rules in the knowledge base using either:
Forward chaining – starts with known facts and derives new facts until a solution is found.
Backward chaining – starts with a goal (possible solution) and works backwards to see if the facts support it.
Output: The system presents one or more possible solutions, often with an explanation.
4. Reasoning Methods – Comparison
Method
Process
Typical Use
Forward Chaining
Data → Rules → New Data → … → Solution
Diagnostic systems where many symptoms lead to a diagnosis.
Backward Chaining
Goal → Rules → Required Data → … → Confirmation
Troubleshooting where a specific fault is hypothesised first.
5. Example Scenarios
Medical Diagnosis: Input patient symptoms → system suggests possible illnesses with confidence levels.
Computer Troubleshooting: User reports error codes → system proposes step‑by‑step fixes.
Financial Planning: User provides income, expenses, goals → system recommends budgeting strategies.
Legal Advice: User describes a situation → system outlines relevant statutes and possible actions.
6. Benefits of Using Expert Systems
Provides expert advice 24/7 without the need for a human specialist.
Ensures consistency – the same input always yields the same reasoning.
Captures and preserves knowledge that might otherwise be lost.
Speeds up decision‑making and reduces errors.
7. Limitations
Knowledge base must be regularly updated; otherwise, advice becomes outdated.
Complex or ambiguous problems may be beyond the system’s capability.
High development cost for building a comprehensive knowledge base.
Users may over‑rely on the system and ignore professional judgement.
8. Role of Expert Systems in ICT Applications
Within the broader ICT landscape, expert systems are used to:
Automate routine decision processes in business (e.g., loan approval).
Support e‑learning platforms with personalised feedback.
Enhance customer service via intelligent chatbots.
Assist in network management by diagnosing faults and recommending fixes.
9. Summary Checklist
Identify the three core components: knowledge base, inference engine, user interface.
Explain forward vs. backward chaining with an example.
List at least two real‑world scenarios where expert systems are applied.
State one benefit and one limitation of expert systems.
10. Sample Examination Questions
Define an expert system and name its three main components.
Explain the difference between forward chaining and backward chaining. Give a suitable example for each.
Describe how an expert system could be used to troubleshoot a printer that will not print.
Discuss two advantages and two disadvantages of relying on expert systems in a business environment.
Suggested diagram: Flowchart showing the cycle of input → inference engine (forward/backward chaining) → output with explanation.