LearnViz CompSci
Home / Learn / Emerging & Frontier Topics / Expert Systems

Advanced Topic 42 of 51 Emerging & Frontier Topics

Expert Systems

Rule-based programs encoding specialist knowledge.

How it works

An expert system encodes the knowledge of human specialists as rules and uses an inference engine to draw conclusions. A knowledge base holds if-then rules and facts, forward chaining starts from known facts and derives consequences, backward chaining starts from a goal and searches for supporting facts, and many systems attach certainty factors to handle uncertainty. They can explain their reasoning by showing the rules used. Famous examples are MYCIN for diagnosing infections and XCON for configuring computers, and rule engines such as CLIPS and Drools remain in use. They dominated AI in the 1980s.

Where it is used

Applications today include business rules, tax and eligibility checks, configuration, diagnostics and compliance.

Common pitfall

Knowledge acquisition from experts is slow, large rule bases become hard to maintain and conflict, and the systems cannot handle situations outside their rules. Machine learning now covers many tasks, but rules still help where explanations are required.

Where it fits in the course

Expert Systems is topic 42 of 51 in Emerging & Frontier Topics, rated Advanced. It follows Ontologies and Semantic Web. The next topic is Multi-Agent Systems.

Practice and explore

How to study this topic

  1. Read the explanation above and restate the definition in your own words.
  2. Open the interactive lesson and answer its mini quiz.
  3. Read the common pitfall, then write a short example that avoids it.
  4. Check your understanding in the Emerging & Frontier Topics quiz, and revisit the topic if you miss a question.

Frequently asked questions

What is Expert Systems?
Rule-based programs encoding specialist knowledge. It belongs to Emerging & Frontier Topics and is an advanced-level topic.
Where do people use Expert Systems?
Applications today include business rules, tax and eligibility checks, configuration, diagnostics and compliance.
What is the most common mistake with Expert Systems?
Knowledge acquisition from experts is slow, large rule bases become hard to maintain and conflict, and the systems cannot handle situations outside their rules.
What should I learn before this topic?
Start with Ontologies and Semantic Web, the topic just before it in Emerging & Frontier Topics.
How can I practice it?
Answer the mini quiz in the interactive lesson, then take the Emerging & Frontier Topics quiz.
Is Expert Systems hard to learn?
It is an Advanced topic. Expect to read the explanation more than once and to connect it with earlier topics. Working through a small example by hand helps most.
How long does it take to learn Expert Systems?
Most learners can read the explanation and finish the interactive lesson in under an hour. Fluency comes from applying the idea, so plan a few short practice sessions with the quiz.
Which topics are related to Expert Systems?
Ontologies and Semantic Web, Multi-Agent Systems, Knowledge Graphs are closely connected. The full list is in the related topics section of this page.
What should I study after Expert Systems?
Continue with Multi-Agent Systems, the next topic in Emerging & Frontier Topics.

Related topics