Difficulty: Easy
Correct Answer: Production rules
Explanation:
Introduction / Context:
Rule-based systems are a cornerstone of symbolic AI. They separate knowledge from control: domain knowledge is written as rules, while an inference engine manages which rules fire and in what order. Understanding what counts as 'procedural domain knowledge' clarifies how these systems capture expert behavior and make decisions transparently.
Given Data / Assumptions:
Concept / Approach:
Production rules are condition–action statements that operationalize knowledge: IF conditions over facts are true, THEN perform actions such as asserting new facts, recommending a decision, or invoking procedures. These rules collectively encode procedural knowledge because they prescribe what to do when certain patterns are recognized in the data. The inference engine (sometimes called a rule interpreter) selects and fires applicable rules according to a conflict-resolution strategy.
Step-by-Step Solution:
Verification / Alternative check:
Classic expert systems (e.g., MYCIN) are documented as sets of production rules coupled with an inference engine; textbooks consistently define procedural domain knowledge in terms of these rules.
Why Other Options Are Wrong:
Rule interpreters: Part of the control mechanism, not the knowledge itself.
Meta-rules: Rules about rules, often guiding control strategies, not domain procedures.
Control rules: Oversee inference control; again not the domain facts/procedures.
None: Incorrect because production rules precisely fit.
Common Pitfalls:
Mixing up the engine (how reasoning happens) with the knowledge base (what is known and how to act).
Final Answer:
Production rules
Discussion & Comments