Artificial intelligence

Computer Science · Automated and emerging technologies. The idea in plain words, the rules to remember, the mistake to avoid and a worked example, written and checked by Mega Lecture.

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The idea

Artificial intelligence (AI) is the branch of computer science that deals with the simulation of intelligent behaviour by computers. An AI system has a collection of data and the rules for using that data. It has the ability to reason, and it can include the ability to learn and adapt.

An expert system copies the knowledge of human experts in one subject, for example diagnosing an illness or a fault in an engine. The user answers questions and the system gives a likely conclusion.

In machine learning, a program can automatically adapt its own processes and/or data. It gets better at a task as it is given more data, without a programmer rewriting it.

Rules to remember

Knowledge base: the facts about the subject, collected from human experts.
Rule base: the set of rules (often in the form IF ... THEN ...) that link the facts.
Inference engine: applies the rules to the facts and to the user's answers to reach a conclusion. It also decides which question to ask next.
Interface: how the user and the system communicate. It shows the questions, takes the answers and displays the result.
Machine learning: the program is trained on data, finds patterns, and changes its own rules or data to improve its results.
Difference: the rules of a basic expert system are set by experts; a machine learning program adapts its own.

Common mistake

Students mix up the knowledge base and the rule base. The knowledge base holds the facts; the rule base holds the rules; the inference engine is the part that uses both.

Worked example

An expert system identifies a type of rock. Name the component that does each job: (1) holds 'granite contains quartz', (2) holds 'IF it fizzes in acid THEN it may be limestone', (3) shows the questions on screen, (4) works out the answer.

(1) is a fact about the subject, so it is in the knowledge base.
(2) is an IF ... THEN ... rule, so it is in the rule base.
(3) is how the user communicates with the system: the interface.
(4) needs the rules to be applied to the facts and the user's answers: the inference engine.

Answer: (1) knowledge base, (2) rule base, (3) interface, (4) inference engine.

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