Which of the following is a core component of an expert system architecture used in artificial intelligence applications?

Introduction / Context:
Expert systems emulate the decision-making ability of human experts. Their architecture is typically modular so that knowledge can be maintained separately from the reasoning mechanism and presented to users in a usable form. Understanding these components is foundational for AI courses and certifications.

Given Data / Assumptions:

• We consider standard, textbook expert system architecture.
• Focus on the high-level components common across many shells.

Concept / Approach:
The knowledge base stores domain facts and rules. The inference engine applies reasoning strategies (forward chaining, backward chaining, conflict resolution) to the knowledge base to derive conclusions. The user interface (and sometimes an explanation facility) gathers inputs, shows conclusions, and explains the reasoning. All three are essential and interdependent in practical systems.

Step-by-Step Solution:
1) Identify knowledge representation: rules/frames present in the knowledge base.2) Identify reasoning mechanism: inference engine executes rules and resolves conflicts.3) Identify interaction layer: UI captures facts and displays advice/explanations.4) Conclude that all listed components are core to an expert system.

Verification / Alternative check:
Check classic shells (e.g., CLIPS, JESS): each exposes these components or their equivalents, sometimes adding an explanation module and a knowledge acquisition tool.

Why Other Options Are Wrong:

• Each single component answer omits other indispensable parts.
• “None of the above” contradicts standard AI architecture.

Common Pitfalls:
Confusing the knowledge base with a database; overlooking the need for user-centric explanation when deploying to non-experts.

Final Answer:
All of the above