Match the following with respect to heuristic search techniques :…
2015
Match the following with respect to heuristic search techniques :
\(\begin{array}{clcl} & \textbf{List – I} && \textbf{List – II} \\ \text{(a)} & \text{Steepest-acccent Hill} & \text{(i)} & \text{Keeps track of all partial paths which can} \\ & \text{Climbing}&&\text{be candiadate for further explaination} \\ \text{(b)} & \text{Branch-and-bound} & \text{(ii)} & \text{Discover problem state(s) that satisfy } \\ & \text{}&&\text{a set of constraints} \\ \text{(c)} & \text{Constraint satisfaction} & \text{(iii)} & \text{Detects difference between current state} \\ & \text{}&&\text{and goal state} \\ \text{(d)} & \text{Means-end-analysis} & \text{(iv)} & \text{Considers all moves from current state} \\ & \text{}&&\text{and selects best move} \\ \end{array}\)
Codes :
- A.
(a)-(i), (b)-(iv), (c)-(iii), (d)-(ii)
- B.
(a)-(iv), (b)-(i), (c)-(ii), (d)-(iii)
- C.
(a)-(i), (b)-(iv), (c)-(ii), (d)-(iii)
- D.
(a)-(iv), (b)-(ii), (c)-(i), (d)-(iii)
Attempted by 59 students.
Show answer & explanation
Correct answer: B
Correct matching and brief explanations:
Steepest-ascent hill climbing → Considers all moves from the current state and selects the best move.
Reason: This local search inspects neighboring states and picks the neighbor with the largest improvement, so it evaluates possible moves and chooses the best.
Branch-and-bound → Keeps track of all partial paths which can be candidates for further expansion.
Reason: Branch-and-bound maintains a frontier of partial solutions with bounds to prune unpromising branches while exploring alternatives.
Constraint satisfaction → Discovers problem state(s) that satisfy a set of constraints.
Reason: Constraint satisfaction problems are solved by finding assignments that meet all constraints, using techniques like backtracking, constraint propagation, or heuristics.
Means-end analysis → Detects the difference between the current state and the goal state and selects actions to reduce that difference.
Reason: Means-end analysis decomposes the problem by identifying subgoals to reduce discrepancies between current and goal states and choosing operators to achieve those subgoals.
Final mapping: (a) Steepest-ascent hill climbing → considers all moves and selects best move; (b) Branch-and-bound → keeps track of partial paths for expansion; (c) Constraint satisfaction → finds states satisfying constraints; (d) Means-end analysis → detects differences between current and goal states.