Means-End Analysis in Artificial Intelligence
The means-end analysis is a special type of knowledge-rich search that allows both backward and forward searching.
Principle of Means-End Analysis:
“It allows us to solve parts of a problem first and then go back and solve the smaller problems that arise while assembling the final solution”.
Technique: It is based on the use of operations which transform the state of the world. MEA works on three primary goals:
Transformation: It means to transform object A into object B. It is an AND graph which subdivided a problem into an intermediate problem and then transforms that problem into the goal state B.
Note: This process terminates when there is no difference between A and B or we can say when the goal is reached.
Reduction: It means to reduce the difference between object-A and object B by modifying object-A.
Note: The goal of the operation is to reduce the difference between object-A and object-B by transforming object-A into object-A’ nearer goal B. This is called Relevant operator (R).
Application: It means to apply the operator R to object-A. This will again be an AND graph showing the goal of reducing the difference between object-A and the pre-conditions required for the operator R, giving intermediate object A”. Operator R is then applied to A”, transforming it to A’, which is close to goal B.
Algorithm for Means-End Analysis (MEA):
Step-1: Until the goal is reached or no more procedure are available.
Step-2: Describe the current state, the goal state and the difference between the two.
Step-3: Use the difference between the current state and goal state, possibly with the description of the current state or goal state, to select a promising procedure.
Step-4: Use the promising procedure and update the current state.
Step-5: If the goal is reached then success else it is a failure.