RULE BASED EXPERT SYSTEM
A system that uses a collection of "if-then" rules to simulate the decision-making process of a human expert in a specific domain.
Rule-Based Expert System
This are the fundamental components of a rule-based expert system, typically expressed as "if-then" statements that dictate the system's behavior.
Production Rules
The component that applies the rules to the facts available in the system, drawing conclusions or making decisions.
Inference Engine
An inference method that starts with known facts and applies rules to infer new facts until a goal is achieved.
Forward chaining
The process of selecting the appropriate rule to apply when multiple rules are triggered simultaneously.
Conflict resolution
The collection of all the rules that a rule-based expert system uses to make decisions or solve problems.
Rule based
a component of the Inference Engine that interprets and executes the rules according to the facts presented.
Rule Interpreter
a software tool or environment that provides the necessary components, such as the inference engine and rule editor, to develop a rule-based expert system.
Shell
Certainty Factor is a numerical measure used to express the confidence in the truth of a fact or the applicability of a rule.
Certainty Factor
a machine learning technique used to automatically generate rules from a dataset for use in a rule-based system.
Rule Induction
refers to the sequential application of rules, either forward or backward, to infer new information or reach a decision.
Chaining
a higher-level rule that governs the application of other rules, often used to manage conflict resolution or prioritize rule execution
Meta - rule
a component that stores all the facts or data that the system knows, which the Inference Engine uses to apply rules.
Fact Base
refers to the challenge of updating a rule-based system with new information while ensuring that relevant facts and rules are correctly maintained.
Frame Problem