Identify applications of expert systems

7 . Expert Systems

Scope of this Note

This note covers Topic 7 – Expert Systems of the Cambridge IGCSE/A‑Level IT (9626) syllabus. The remaining AS topics (1‑6, 8‑11) and the additional A‑Level topics (12‑21) are taught separately.

Learning Objective

Identify the range of real‑world applications in which expert systems are used and understand the key concepts that underpin their design, operation, testing and maintenance.

What Is an Expert System?

An expert system is a computer program that mimics the decision‑making ability of a human expert. It combines a knowledge base of facts, concepts and heuristics with an inference engine that applies those rules to solve problems within a well‑defined domain.

Key Components

• Knowledge Base – Stores domain‑specific facts, concepts and IF…THEN rules.
• Inference Engine – Performs logical reasoning (forward or backward chaining) to draw conclusions.
• User Interface – Allows users to enter data and receive advice.
• Explanation Facility – Shows the reasoning trace (which rules fired and why) so users can understand and trust the system’s conclusions.

Reasoning Methods

• Forward (data‑driven) chaining – Starts with the known facts and applies rules to infer new facts until a goal is reached.
  Example: IF symptom = fever AND cough THEN possible = flu
• Backward (goal‑driven) chaining – Starts with a hypothesis (goal) and works backwards, asking for facts that support it.
  Example: To prove “patient has flu”, the system asks “Does the patient have fever?” and “Does the patient have cough?”
• Clarification – In the expert‑system literature, forward chaining is also called *data‑driven* reasoning, while backward chaining is called *goal‑driven* reasoning.

Knowledge Representation

Beyond simple IF…THEN rules, expert systems can use:

• Decision Trees – Useful when the problem can be expressed as a sequence of yes/no questions.
• Frames – Structured records that group related attributes (e.g., a “patient” frame containing age, symptoms, test results).
• Semantic Networks – Nodes and links that model relationships such as “is‑a” or “part‑of”.

Building an Expert System

1. Knowledge acquisition – Interview experts, observe work, study manuals, and extract rules.
2. Knowledge representation – Choose the most suitable format (rules, decision tree, frames, semantic net) and encode the knowledge.
3. Implementation – Load the encoded knowledge into the knowledge base and connect it to the inference engine.
4. Maintenance workflow
   • Review the knowledge base at least quarterly.
   • Log every change (who, what, why, date).
   • Re‑run validation tests after each update.
   • Version‑control the knowledge base to allow rollback if needed.

Testing & Evaluation

To ensure the system is reliable, follow this checklist:

Typical Application Areas

Expert systems are employed wherever specialised knowledge must be applied quickly, consistently and at a lower cost than human experts.

Why Use Expert Systems?

• Capture and preserve scarce expert knowledge.
• Provide consistent, unbiased decisions.
• Operate continuously without fatigue.
• Enable training and knowledge transfer.
• Offer an explanation facility that builds user confidence.

Limitations & Advanced Considerations

• Knowledge acquisition cost – Gathering and formalising expertise can be time‑consuming.
• Maintenance requirement – Out‑of‑date knowledge reduces accuracy; regular updates are essential.
• Rule‑conflict resolution – When several rules fire, the system uses strategies such as specificity, recency or explicit priority to choose the best conclusion.
• Scalability – Large knowledge bases may slow inference; optimisation or modular design may be needed.
• Hybrid systems – Modern solutions often combine rule‑based reasoning with machine‑learning models to handle uncertain or data‑intensive tasks.
• User over‑reliance – Users may accept advice without critical evaluation; training on the system’s limits is important.

Summary Checklist for Identifying Applications

• Is the problem domain well‑defined and rule‑based?
• Does the task require specialised expertise that is scarce or expensive?
• Can decisions be expressed as logical IF…THEN rules, decision trees, frames or semantic nets?
• Is there a need for consistent, repeatable outcomes?
• Will users benefit from explanations of the reasoning process?
• Are there provisions for regular maintenance and testing?
• Would a hybrid approach (rules + ML) improve performance?