How to represent problems in Artificial Intelligence?

Before, a solution can be found; the prime condition is that the problem must be very precisely defined.

By defining it properly, one converts the abstract problem into real workable stats that are really understood. These stats are operated upon by a set of operators and the decision of which operator to be applied, when and where is dictated by the overall control strategy.

So to build a system to solve a particular problem, we need to do four things:

  • Define the problem precisely: The definition must include precise specifications of what the initial situation will be as well as what final situation constitutes acceptable solutions to the problem.
  • Analyze the problem: A few very important features can have an immense impact on the appropriateness of various possible techniques for solving the problem.
  • Isolate and represent the task knowledge is the necessary to solve the problem.
  • Choose the best problem- solving technique and apply it (them) to the particular problem.

The most common methods of problem representation in Artificial Intelligence are:

  1. State Space representation
  2. Problem reduction

Definition of State Space representation:

“A Set of all possible states for a given problem is known as the state space of the problem”