UGC NET CS 2012 June-Paper2
April 5, 2024ICT
April 5, 2024Artificial-Intelligence
| Question 36 |
Match List I with List II
List I List II
A) Branch-and-bound (I) Keeps track of all partial paths which can be can be a candidate for further exploration.
B) Steepest-ascent hill climbing (II) Detects difference between current state and goal state.
C) Constraint satisfaction (III) Discovers problem state(s) that satisfy a set of constraints.
D) Means-end-analysis (IV) Considers all moves from current state and selects the best move.
Choose the correct answer from the options given below:
List I List II
A) Branch-and-bound (I) Keeps track of all partial paths which can be can be a candidate for further exploration.
B) Steepest-ascent hill climbing (II) Detects difference between current state and goal state.
C) Constraint satisfaction (III) Discovers problem state(s) that satisfy a set of constraints.
D) Means-end-analysis (IV) Considers all moves from current state and selects the best move.
Choose the correct answer from the options given below:
| A-I, B-IV, C-III, D-II | |
| A-I, B-II, C-III, D-IV
| |
| A-II, B-I, C-III, D-IV | |
| A-II, B-IV, C-III, D-I |
Question 36 Explanation:
Branch-and-bound→ Keep track of all partial paths which can be a candidate for further exploration.
Steepest-ascent hill climbing → Considers all moves from current state and selects the best move.
Constraint satisfaction → Discovers problem state(s) that satisfy a set of constraints.
Means-end-analysis → Detects difference between current state and goal state.
Steepest-ascent hill climbing → Considers all moves from current state and selects the best move.
Constraint satisfaction → Discovers problem state(s) that satisfy a set of constraints.
Means-end-analysis → Detects difference between current state and goal state.
Correct Answer: A
Question 36 Explanation:
Branch-and-bound→ Keep track of all partial paths which can be a candidate for further exploration.
Steepest-ascent hill climbing → Considers all moves from current state and selects the best move.
Constraint satisfaction → Discovers problem state(s) that satisfy a set of constraints.
Means-end-analysis → Detects difference between current state and goal state.
Steepest-ascent hill climbing → Considers all moves from current state and selects the best move.
Constraint satisfaction → Discovers problem state(s) that satisfy a set of constraints.
Means-end-analysis → Detects difference between current state and goal state.