Expert systems architecture: which of the following are components typically found in a knowledge-based expert system?

Introduction / Context:
Expert systems are AI applications that emulate decision-making of human experts in narrow domains. They rely on encoded knowledge and reasoning mechanisms to infer conclusions from user-provided facts. The question asks which listed elements are components of such systems.

Given Data / Assumptions:

• An expert system must store domain knowledge in some structured form.
• It needs a reasoning engine to apply rules or heuristics to facts.
• Usability features such as explanations and natural language I/O are common components that improve trust and accessibility.

Concept / Approach:
Core architecture includes a knowledge base (rules, frames, facts), an inference engine/machine (forward or backward chaining), and an explanation module (why/how a conclusion was reached). A natural language interface is not strictly mandatory in every implementation, but it is a recognized component that many systems include to interact with users more naturally. Therefore, all four items fit within typical expert system architectures.

Step-by-Step Solution:

Verification / Alternative check:
Classic AI texts describe expert systems with these blocks; many shells (for example, CLIPS-like systems) also provide explanation facilities and optional NLP front ends.

Why Other Options Are Wrong:

Common Pitfalls:
Confusing machine learning systems (which learn from data) with rule-based expert systems (which encode explicit knowledge); the component lists differ.

Final Answer:
All of the above