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Match $\text{List I}$ with $\text{List II}$

With reference to CMM developed by Software Engineering Institute (SEI)

$\begin{array}{llll} & \text{List I} && \text{List II} \\ (A) & \text{Branch-and-bound} & (I) & \text{Keeps track of all partial paths} \\ &&& \text{which can be candidate for} \\ &&& \text{further exploration.} \\ (B) & \text{Steepest-accent hill climbing} & (II) & \text{Defects difference between current} \\ &&& \text{state and goal state} \\ (C) & \text{Constraint satisfaction} & (III) & \text{Discovers problem state(s) that satisfy} \\ &&& \text{a set of constraints} \\ (D) & \text{Means-end-analysis} & (IV) & \text{Considers all moves from current state} \\ &&& \text{and selects best move} \end{array}$

Choose the correct answer from the options given below:

  1. $\text{A-I, B-IV, C-III, D-II}$
  2. $\text{A-I, B-II, C-III, D-IV}$
  3. $\text{A-II, B-I, C-III, D-IV}$
  4. $\text{A-II, B-IV, C-III, D-I}$
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The Means-end-analysis technique is a strategy to control search in problem-solving. Given a current state and a goal state, an action is chosen which will reduce the difference between the two. The action is performed on the current state to produce a new state, and the process is recursively applied to this new state and the goal state.

Constraint satisfaction problems  are mathematical questions defined as a set of objects whose state must satisfy a number of  constraints.

The general idea of Branch and bound algorithm  is a BFS-like search for the optimal solution, but not all nodes get expanded . Rather, a carefully selected criterion determines which node to expand and when, and another criterion tells the algorithm when an optimal solution has been found.

Steepest-Ascent Hill climbing: It first examines all the neighboring nodes and then selects the node closest to the solution state as of next node

so clearly option  A is correct ans

 

Answer:

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