IGCSE Computer Science Paper 1 Definitions Worksheet: 6.3 Artificial Intelligence (Modules 662–678)
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Key terms
Write what each term means, in your own words.
1
specialist knowledge
2
knowledge base, rule base, inference engine, interface
3
knowledge base
4
rule base
5
inference engine
6
user interface
7
interface -> inference engine -> interface
8
trace an expert system
9
machine learning
10
adaptive processes
11
self-updating data
12
Expert system vs machine learning
13
classify AI system
14
AI system operation
15
key term + reason
Answer Key
IGCSE Computer Science Paper 1 · Modules 662–678 · Student wording will vary for definitions
1specialist knowledge — The expert knowledge that expert systems supply in areas like medical diagnosis, fault finding, mineral prospecting and financial advice, so the user does not need to be an expert.
2knowledge base, rule base, inference engine, interface — The four parts of an expert system: the knowledge base holds facts, the rule base holds rules, the inference engine reasons, and the interface talks to the user.
3knowledge base — The part of an expert system that stores facts and information about a subject, gathered from human experts. It can be updated with new facts. The rule base holds the rules.
4rule base — The part of an expert system holding IF-THEN rules (IF condition THEN conclusion) that link facts to conclusions, often joined with AND/OR, which the inference engine uses to reach answers.
5inference engine — The part of an expert system that reasons: it matches the user's answers and the knowledge base against the rule base to reach a conclusion. It is the 'brain' of the system.
6user interface — The part of an expert system the user sees. It shows questions, lets the user enter answers and facts, and displays the conclusion in a simple way for non-experts.
7interface -> inference engine -> interface — The user answers questions at the interface; the inference engine applies the rules to the answers and knowledge base; the interface then shows the result.
8trace an expert system — Follow the user's answers through the IF-THEN rules in order, checking which rules fire and which do not, until one rule gives the final conclusion.
9machine learning — A type of AI where a program automatically adapts its own processes and/or data, improving with experience, with no programmer changing it.
10adaptive processes — A machine learning program changes its own processes, meaning how it makes decisions, using what happened before, so no human has to rewrite the code.
11self-updating data — A machine learning program changes its own stored data by adding, updating or removing items as it meets new cases, e.g. spam marked by users, and uses that data in later decisions.
12Expert system vs machine learning — An expert system reasons using rules and facts put in by experts. Machine learning adapts its own processes and data by itself. Both are types of AI.
13classify AI system — Decide if a system gives advice from a knowledge base and rule base (expert system) or changes its own processes or data (machine learning), then justify with because.
14AI system operation — Naming the parts of an AI system, what each does, and how they work together to take input, reason on data and rules or learn, and output a result that simulates intelligence.
15key term + reason — Use exact key terms (data, rules, reasoning, knowledge base, rule base, inference engine, interface, machine learning) and link each point to a reason.