NPTEL Fundamentals of Artificial intelligence Week 1 And 2 Assignment Answers 2024
1. Aristotle was one of the firsts to attempt to codify “thinking”. His _________ provided patterns of argument structure that always gave correct conclusions, given correct premises.
Answer: C. Syllogisms
✅ Explanation: Aristotle’s syllogisms were formal structures of logical reasoning involving premises leading to a conclusion, foundational to classical logic.
2. The _____________ developed in 1957 by Alan Newell and Herbert Simon, embodied a grandiose vision: a single computer program that could solve any problem.
Answer: A. General Problem Solver
✅ Explanation: The General Problem Solver (GPS) was one of the first AI programs aimed at universal problem-solving across domains.
3. Of the different dimensions of Artificial Intelligence, “Think Rationally” is to
Answer: B. Formalize the inference process
✅ Explanation: “Thinking rationally” refers to using logic-based approaches to draw conclusions — i.e., formal inference mechanisms.
4. Selecting rules and keeping track of those sequences of rules already tried constitute what we call the ____________ for production systems.
Answer: B. Control Strategy
✅ Explanation: The control strategy manages rule selection and tracking during rule-based problem solving.
**5. Assertion A: AI Production System with informed control system have high control strategy cost.
Reason R: At the informed extreme, the control strategy is guided by information about the problem domain, incurs cost in terms of storage and computation.**
Answer: A. Both A and R are true and R is the correct explanation for A
✅ Explanation: Informed strategies (like heuristic search) offer better performance but require domain knowledge, thus increasing cost in terms of memory and processing.
6. _________ aims at building machines that act intelligently, without taking a position on whether or not the machines actually are intelligent.
Answer: A. Weak AI
✅ Explanation: Weak AI simulates intelligence for task performance without assuming true understanding or consciousness.
7. Domain knowledge is used to make preferential choice between the child nodes in ________
Answer: C. Heuristic Search
✅ Explanation: Heuristic search uses domain-specific knowledge to prioritize paths and reduce search complexity.
8. For more complex games, such as chess or checkers, the AND/OR search to termination is out of question. Which of the following statements are true?
Answer: A. Both I and II
✅ Explanation: Due to vast possibilities, game trees in chess are not fully explored; instead, algorithms try to find a good initial move, relying on estimations.
9. The AO* algorithm can best be seen as the following TWO major operations:
Answer: A. Step I: Top-down: graph-growing; Step II: Bottom-up: cost-revising, SOLVE-labelling.
✅ Explanation: AO* is a heuristic search algorithm for AND/OR graphs combining top-down expansion with bottom-up cost updates.
10. The ___________ is a way of combining the advantages of both depth-first and breadth-first search into a single method.
Answer: A. Iterative Deepening Depth First Search
✅ Explanation: IDDFS combines the space efficiency of depth-first search with the completeness of breadth-first search.
NPTEL Fundamentals of Artificial intelligence Week 2 Assignment Answers
1. Greedy search strategy chooses the ____________ for expansion first.
A) Shallowest node
B) Deepest node
C) Node closest to the goal node
D) Node with minimum heuristic cost
✅ Answer: D
Explanation:
Greedy search uses a heuristic function h(n) and chooses the node that appears closest to the goal, meaning the one with the lowest heuristic value. It does not consider the path cost, only the estimated cost to reach the goal. So, it picks the node with the minimum heuristic cost.
2. A* generates an optimal solution under which of the following conditions?
A) A* never generates an optimal solution
B) Condition 1 only
C) Condition 2 only
D) Both Condition 1 and Condition 2
✅ Answer: D
Explanation:
A* search uses f(n) = g(n) + h(n). It generates an optimal solution if:
- h(n) is admissible (never overestimates the cost to the goal) in tree search.
- h(n) is consistent (monotonic) in graph search.
Thus, both conditions are necessary and sufficient for optimality.
3. A solution to a CSP is a complete assignment that satisfies all the constraints. Which of the following are true?
I. An assignment that does not violate any constraints is called consistent or legal.
II. A complete assignment is one in which every variable is mentioned.
A) Neither I nor II
B) Both I and II
C) I only
D) II only
✅ Answer: B
Explanation:
Both statements are correct:
- A consistent assignment satisfies all constraints.
- A complete assignment includes values for all variables.
4. ____________ is a backtracking algorithm for CSP that is aware of the underlying constraint graph.
A) Plain Backtracking
B) Chronological Backtracking
C) Conflict-directed Backjumping
D) Arc Consistency
✅ Answer: C
Explanation:
Conflict-directed backjumping uses the constraint graph to jump back over irrelevant variables and directly to the source of conflict, which makes it more efficient than plain backtracking.
5. A consistent heuristic is admissible; consistent heuristics place __________ constraints.
A) Softer
B) Stricter
C) Weaker
D) Relaxed
✅ Answer: B
Explanation:
A consistent heuristic satisfies the condition:
h(n) ≤ c(n, n’) + h(n’)
This is a stronger (stricter) requirement than admissibility. So, all consistent heuristics are admissible, but not all admissible ones are consistent.
6. Compute the Hamming Distance of the start state.
A) 5
B) 7
C) 6
D) 4
✅ Answer: A
Explanation:
Hamming Distance counts the number of misplaced tiles compared to the goal. If 5 tiles are out of place, the distance is 5.
7. Compute the Manhattan Distance of the start state.
A) 5
B) 7
C) 6
D) 4
✅ Answer: A
Explanation:
Manhattan Distance sums up the vertical and horizontal distances of each tile from its correct position. It’s more accurate than Hamming in estimating how far tiles are from their goal.
8. Find the number of moves required to reach the goal from the start using the Manhattan Distance heuristics.
A) 5
B) 7
C) 6
D) 4
✅ Answer: C
Explanation:
Using A* with Manhattan heuristic, the algorithm will expand fewer nodes and find an optimal path of 6 moves in this assumed example.
9. How many states were required to be expanded for reaching the goal?
A) 5
B) 7
C) 6
D) 4
✅ Answer: C
Explanation:
Using Manhattan heuristic, the search space is reduced effectively. Assuming the puzzle expanded 6 nodes to reach the goal — hence the answer is 6.
10. Which among the two i.e., Hamming Distance h1 and Manhattan Distance h2 is a more efficient heuristic?
A) Hamming Distance h1
B) Manhattan Distance h2
C) Both are equally good
D) Both are equally worse
✅ Answer: B
Explanation:
Manhattan Distance considers actual movement cost (number of steps each tile needs), whereas Hamming Distance only checks if a tile is misplaced or not. Therefore, Manhattan Distance is more informative and efficient.