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The mean-end analysis process centers around the detection of differences between the current state and goal state. Once such a difference is isolated, an operator that can reduce the difference must be found. But perhaps that operator cannot be applied to the current state. So a sub-problem of getting to a state in which it can be applied is set up. The kind of backward chaining in which operators are selected and then sub goals are set up to establish the precondition of operators is called

  1. backward planning
  2. goal stack planning
  3. operator subgoaling
  4. operator overloading
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ans is C operator subgoaling

If an operator has been selected but cannot be applied to the current state,
a useful strategy is to create a subgoal to find a state where the operator
can be applied.  This strategy is called *operator subgoaling* (also
{precondition satisfaction}) and is a common AI technique

Goal stack planning One of the earlier planning algorithms called goal stack planning. It was used by STRIPS. We work backwards from the goal, looking for an operator which has one or more of the goal literals as one of its effects and then trying to satisfy the preconditions of the operator. The preconditions of the operator become subgoals that must be satisfied. We keep doing this until we reach the initial state. Goal stack planning uses a stack to hold goals and actions to satisfy the goals, and a knowledge base to hold the current state, action schemas and domain axioms Goal stack is like a node in a search tree; if there is a choice of action, we create branches

operator overloading is a different concepts used in OOPS

For forward search which is data oriented we starte at the initial state and compute state transitions 

For backward search which is goal oriented, we start at the goal and compute inverse state transitions

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