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Artificial Intelligence: Search Methods for Problem Solving Week 3 NPTEL Assignment Answers 2025

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NPTEL Artificial Intelligence: Search Methods for Problem Solving Week 3 Assignment Answers 2025

1. In the map, list the first 7 nodes (including S that are) inspected by the Breadth First Search (BFS) algorithm. List the nodes in the order they were inspected. If the algorithm terminates early then list the nodes inspected up until termination.

Enter a comma separated list of node labels.
DO NOT ENTER SPACES, TABS, BRACKETS OR EXTRANEOUS CHARACTERS.
Answer format: A,B,C

Answer : See Answers

2. Which node has the largest heuristic value? What is its heuristic value? Remember to use the Manhattan distance. When multiple nodes qualify then choose any one node.

Enter the node label and the heuristic value as a comma separated list.
DO NOT ENTER SPACES, TABS, BRACKETS OR EXTRANEOUS CHARACTERS.
Answer format: A,17

Answer :

3. In the map, list the first 7 nodes (including S that are) inspected by the Best First Search algorithm. During inspection if multiple candidate nodes have the same estimated cost then inspect those nodes in alphabetical order.

List the nodes inspected in the order they were inspected. If the algorithm terminates early then list the nodes inspected up until termination.

Enter a comma separated list of node labels.
DO NOT ENTER SPACES, TABS, BRACKETS OR EXTRANEOUS CHARACTERS.
Answer format: A,B,C

Answer :

4. In the map, list the first 7 nodes (including S that are) inspected by Hill Climbing algorithm.
During inspection if multiple nodes have the same cost then inspect those nodes in alphabetical order.

List the nodes in the order they were inspected.
If the algorithm terminates early then list the nodes inspected up until termination.

Enter a comma separated list of node labels.
DO NOT ENTER SPACES, TABS, BRACKETS OR EXTRANEOUS CHARACTERS.
Answer format: A,B,C

Answer :

5. For the given map, which of the algorithms find a path from node S to node G?

  • Depth First Search
  • Breadth First Search
  • Hill Climbing
  • Best First Search
Answer :

6. For the given map, try and find a TSP tour using ONLY the edges in the map. Choose the correct options.

  • I could not find a tour
  • No one can find a tour
  • I found one tour
  • There is definitely more than one tour
Answer : See Answers

7. For the given city map, starting from S and using ONLY the given edges and using Euclidean distance as the edge cost, construct a TSP tour using the Nearest Neighbour algorithm. Use node labels to break ties.

  • Nearest Neighbour algorithm fails to find a tour
  • Nearest Neighbour algorithm finds a tour
  • Nearest Neighbour algorithm finds an optimal tour
Answer :

8. Consider a 4 variable SAT formula

F = (a) ∧ (a ∨ ¬b ∨ ¬c) ∧ (¬a ∨ c ∨ d) ∧ (¬b ∨ c ∨ ¬d) ∧ (¬a ∨ b) ∧ (c ∨ d);

Let S=”1000” be the start node, then Sa=”0000”, Sb=”1100”, Sc=”1010” and Sd=”1001”are the neighbours generated by the change-one-bit MoveGen function. Use the number of clauses satisfied as the heuristic function. Observe that this is a maximization problem.

Compute the heuristic values of S, Sa, Sb, Sc, Sd. Enter the heuristic values of S, Sa, Sb as a comma separated list.

Answer :

9. Consider using Beam Search with width=2 to solve the above SAT problem. Candidate “1000” is the start node at level 1. Now, list the two nodes in level 2 that will be placed in the beam.

When multiple nodes have the same cost, use the node label (4-bit string ascending) to break the tie.

Answer :

10. Continue from the previous question. Determine the two nodes in level 3 that will be placed in the beam.

Enter the heuristic values of the two nodes (from level 3 that are placed in the beam) as a comma separated list in ascending order.

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11. What is the variable assignment that makes the given SAT formula true? Enter the assignment (4-bit string) in the text box. If multiple assignments exist then pick one, any one.

Answer :

12. Select the correct statements.

  • A heuristic function computes an estimate of the distance between a node and its nearest goal node.
  • A heuristic function computes the actual distance between a node and its nearest goal node.
  • Heuristic search always finds the optimal solution.
  • Heuristic search is goal directed.
Answer :

13. Given a finite state space or a finite solution space, which of these algorithms will always find a path/solution if one exists?

  • Depth First Search
  • Breadth First Search
  • Best First Search
  • Beam Search using w = 2
  • Hill Climbing
  • Tabu Search
Answer : See Answers

14. Working with the 2-City-Exchange or the 2-Edge-Exchange operator means that:

  • one is using a Constructive method to solve the TSP
  • one is searching for the solution in the State Space
  • one is using a Perturbative method to solve the TSP
  • one is searching for the solution in the Solution Space
Answer :

15. The Iterated Hill Climbing algorithm

  • works only for planning problems
  • works only for configuration problems
  • can work for both planning and configuration problems
  • is one of the approaches for State Space Search
  • is one of the approaches for Solution Space Search
Answer :

16. The Hill Climbing algorithm may run into a local optimum because _____________.

  • the heuristic function is not informed enough
  • the heuristic function overestimates the distance to the goal
  • the MoveGen (neighbourhood) function does not connect the node to a better neighbour
  • the MoveGen function does not generate reversible moves
Answer :

17. The Variable Neighbourhood Descent ___________.

  • is an extension of Hill Climbing
  • attempts to escape from local optima by using a different heuristic function
  • attempts to escape from a local optima by using a different MoveGen function which is more dense
  • sometimes allows moves against the heuristic function
Answer : See Answers

18. Simulated Annealing _____________.

  • generates all the neighbours and moves to the best neighbour
  • generates one random neighbour and will definitely move to it if it is better
  • generates one random neighbour and may possibly move to it whether it is better or worse
  • generates one random and will definitely not move to it if it is worse
Answer :

19. Simulated Annealing _____________.

  • begins with more random moves and gradually decreases randomness
  • begins with deterministic moves and gradually introduces randomness in its moves
  • employs a parameter called temperature that is decreased monotonically
  • employs a parameter called temperature that is increased monotonically
  • employs a parameter called temperature that is increased and decreased in alternate iterations
Answer :

20. In stochastic local search _____________.

  • random walk introduces exploitation in search
  • random walk introduces exploration in search
  • gradient descent introduces exploitation in search
  • gradient descent introduces exploration in search
Answer : See Answers

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