Artificial Intelligence
Automated and Emerging Technologies · 4 question types
Exam Frequency Analysis
Past paper frequency (2018 to 2024)
This topic accounts for approximately 4% of your exam marks.
AI applications and machine learning concepts are growing in exam prominence.
An expert system is an AI program that imitates the decision-making of a human expert in a specific field, by combining a body of expert knowledge with logical rules.
Examples of expert systems:
- Medical diagnosis systems that suggest possible conditions from a patient's symptoms.
- Equipment troubleshooting systems that walk a technician through finding faults.
- Technical support chatbots that diagnose customer problems.
- Tax advice software that asks questions and recommends actions.
- Geological prospecting systems that suggest where to drill for oil or minerals.
The four components of an expert system
A standard expert system has four parts working together. Exam questions often ask you to name and describe each.
| Component | What it holds / does |
|---|---|
| Knowledge base | A store of facts in the chosen field, gathered from real human experts. For a medical system: lists of diseases, symptoms and test results |
| Rule base | A set of rules (typically IF-THEN statements) that link facts together: "IF cough AND fever AND chest-pain THEN suspect pneumonia" |
| Inference engine | The program that applies the rules to the facts and derives conclusions. It is the "reasoning" component |
| User interface | The way the user interacts with the system: typically a series of questions and a final answer or recommendation |
A typical session: the user enters information through the user interface; the queries the rule base and the to work out what the user is dealing with; the system asks follow-up questions or presents conclusions back through the interface.
Expert system components
Identifying the parts of an expert system appears in most papers, so you need to know the four: knowledge base (facts), rule base (IF-THEN rules), inference engine (applies the rules to the facts), and user interface. "Mechanical engine" and "question base" are distractors, not real components.
Advantages of expert systems
| Advantage | Why it matters |
|---|---|
| Consistent results | Every query is treated the same way, with no fatigue or mood changes |
| Faster responses | Far quicker than waiting for a human expert |
| Stores huge knowledge bases | Can hold more facts than any single human could remember |
| Available 24/7 | No waiting lists, no shift hours |
| Unbiased (when designed well) | Does not have personal preferences, although it may carry biases from its source data |
| Used as a training tool | Junior staff can learn from the system's explanations and recommendations |
Disadvantages of expert systems
| Disadvantage | Why it matters |
|---|---|
| Only as good as the knowledge entered | If the knowledge base is incomplete or out of date, the recommendations will be wrong |
| Lacks human empathy and judgement | A medical expert system cannot reassure a frightened patient or notice non-verbal cues |
| Requires training to use correctly | Users must enter information accurately; mistakes lead to misleading answers |
| Building it is expensive | Capturing expert knowledge in formal rules takes time and a real expert's involvement |
| Cannot easily handle unusual cases | Outside the situations the rules cover, the system may give a confidently wrong answer |
| Maintenance burden | New medical research, new equipment fault types or new tax rules all require the knowledge base and rule base to be updated |