Trace an expert system
Follow a knowledge base and rule base through an inference engine to a user-facing conclusion.
Section 6 · Automated and emerging technologies
This sequence follows Cambridge IGCSE Computer Science 0478, 2026–2028 syllabus Version 6. It covers subject content sections 6.1–6.3 only. The separately published 2029–2031 syllabus is not mapped here. Practice is original, self-checked and does not award an official Cambridge grade. Full timed papers and human subject-teacher review remain outstanding.
Before you begin
- No programming language is required. Be comfortable with place value and multiplication; each lesson introduces the computer-science notation it uses.
Work on paper. Try each question before revealing help, and explain why your method works.
Quick readiness check
Where are the facts in an expert system stored?
What you will learn
- Identify the four expert-system components
- Explain how facts and rules produce an inference
- Describe how the interface communicates the result
An expert system applies stored expertise
An expert system contains a knowledge base of facts, a rule base of conditional instructions, an inference engine that applies rules, and an interface for the user. Together these components simulate specialist decision-making within a defined area.
Apply and check the rule
The user supplies information through the interface. The inference engine compares it with the knowledge base and rule base, then returns a conclusion or advice through the interface. The conclusion depends on the quality and coverage of stored knowledge; it is not a guarantee that a human expert has checked the case.
Pause and explain
Which component presents a result to a user?
Worked example
A simple plant expert system receives “leaves are yellow” and “soil is wet.” A rule says yellow leaves plus wet soil may indicate overwatering. Trace the system.
Show the worked solution
- The interface receives the observations.
- The inference engine checks the facts and applies the rule.
- The system displays a possible overwatering conclusion.
Answer The interface accepts the observations; the inference engine matches them to the knowledge and rule bases; the interface presents a possible overwatering result.
Try a topic test
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Write your answer and reasoning first. Use a hint only if you are stuck. The model solution is for self-checking; your written work is not automatically marked.
Match knowledge base, rule base, inference engine and interface to their roles.
Give me a hint
Separate stored facts, conditions, processing and communication.
Compare my reasoning
- Knowledge base: facts.
- Rule base: conditional rules.
- Inference engine applies rules; interface accepts information and presents the result.
Knowledge base stores facts; rule base stores conditions; inference engine applies them; interface communicates with the user.
Look for these in your work
- Facts
- Rules
- Processing and communication
An expert system has a rule “if temperature is high and pressure is low, show alert.” Explain how the inference engine uses two sensor facts.
Give me a hint
It must match both conditions before triggering the rule.
Compare my reasoning
- The knowledge base contains the current readings.
- The inference engine checks both conditions against the rule base.
- If both match, it returns the alert through the interface.
It compares the readings with the rule; if both conditions match, the system presents the alert.
Look for these in your work
- Facts read
- Rule checked
- Output
An expert system recommends a treatment based on an incomplete knowledge base. Explain why its answer should be reviewed and identify the components involved.
Give me a hint
The inference process cannot exceed stored knowledge.
Compare my reasoning
- The knowledge base may omit relevant facts.
- The inference engine can only apply rules that are present.
- A professional should review the result and add relevant information through the interface.
The system may miss an important condition because its knowledge or rules are incomplete. The inference engine cannot infer beyond them, so a qualified person should review the advice.
Look for these in your work
- Knowledge limitation
- Inference limitation
- Human review
Common mistakes
- Calling the inference engine the database
- Treating an expert system's output as a guaranteed diagnosis
What can you explain now?
Which component applies rules to facts?
Compare with the explanation
Inference engine
It uses the knowledge base and rule base to derive a response.
After trying it yourself, choose your next review. This is your self-assessment.
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Make it stick
Name the four components of an expert system and the role of each.
If you needed the solution, close it and solve the problem again from a blank page. A correct answer is stronger when you can explain the reason for each step.