(a) Explain how the inference engine in an expert system reaches a conclusion. [2] (b) State two types of reasoning that an inference engine might use. [2] ...

(a) The inference engine reaches a conclusion by taking the user's answers and input and comparing them against the rules stored in the knowledge base. [1] It then chains through the rules logically, narrowing down the possibilities step by step until it arrives at a final conclusion or recommendation. [1] This process is systematic: each answer eliminates certain paths and opens others, much like a decision tree.

(b) Two types of reasoning an inference engine might use:

• Forward chaining: the engine starts with known facts (the user's input) and applies rules to derive new facts, continuing until a conclusion is reached. [1]
• Backward chaining: the engine starts with a possible conclusion (a hypothesis) and works backward through the rules to see whether the known facts support that conclusion. [1]

(c) The explanation facility is important because it shows the user how the conclusion was reached and which specific rules were applied during the reasoning process. [1] This transparency allows the user to verify that the reasoning is correct and to make an informed decision about whether to accept or question the advice given. [1] Without it, the system would be a "black box" and users would have no basis for trusting its output.

(d) One key difference is that an expert system can reason and make recommendations based on rules, while a standard database simply stores, retrieves and organises data without any decision-making capability. [1]