Problem Representation in AI

To solve problems effectively, AI systems must represent knowledge in a machine-understandable form.
This involves defining:

• States: Descriptions of the world at a point in time.
• Operators: Actions that change states.
• Initial State: Where the agent starts.
• Goal State: Desired outcome.

A good representation captures only the essential details, avoiding unnecessary complexity.

For instance, in a chess game, the AI doesn’t store every historical move — it focuses only on the current board configuration, possible legal moves, and winning conditions.
This abstraction allows AI to reason effectively without getting lost in irrelevant data.