What is a rule-based system in AI?

It is an AI system that represents knowledge as explicit facts and rules, then uses an inference engine to derive conclusions or controlled actions. Its behaviour depends on rule semantics, supplied facts and conflict policy.

What are the main components of a rule-based system?

The main components are facts or working memory, a rule or knowledge base, an inference engine, conflict resolution, an input interface and an explanation or trace facility. Production use also requires governance and monitoring.

What is a rule-based inference engine?

It matches facts to rule conditions, creates candidate rules, resolves conflicts, fires selected rules and updates the reasoning state until it reaches a result or stopping condition.

What is the difference between forward and backward chaining?

Forward chaining starts with facts and derives consequences, making it data-driven. Backward chaining starts with a goal and searches for rules and facts that could establish it, making it goal-driven.

What is an example of rule-based AI?

A bounded support-triage assistant can validate identity, recognise a confirmed outage, request missing diagnostic evidence and route a case using explicit rules while preserving reason codes and human escalation.

Are rule-based systems still useful with machine learning?

Yes. Rules remain useful for explicit policy, constraints, routing and explanations. They can govern how a learned score is used, but the model and the complete hybrid decision both require validation and monitoring.

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