NPTEL Fundamentals of Artificial Intelligence Assignment 2 Answers 2022
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NPTEL Fundamentals of Artificial Intelligence Assignment
ABOUT THE COURSE :
What does automatic scheduling or autonomous driving have in common with web search, speech recognition, and machine translation? These are complex real-world problems that span across various practices of engineering! Aim of artificial intelligence (AI) is to tackle these problems with rigorous mathematical tools. The objective of this course is to present an overview of the principles and practices of AI to address such complex real-world problems. The course is designed to develop a basic understanding of problem solving, knowledge representation, reasoning and learning methods of AI.
Next Week Assignment Answers
This course can have Associate in Nursing unproctored programming communication conjointly excluding the Proctored communication, please check announcement section for date and time. The programming communication can have a weightage of twenty fifth towards the ultimate score.
- Assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
- ( All assignments in a particular week will be counted towards final scoring – quizzes and programming assignments).
- Unproctored programming exam score = 25% of the average scores obtained as part of Unproctored programming exam – out of 100
- Proctored Exam score =50% of the proctored certification exam score out of 100
UNPROCTORED PROGRAMMING EXAM SCORE >=10/25 AND PROCTORED EXAM SCORE >= 20/50.
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Q1. The A* search algorithm is based on ___________
A. Breadth-First-Search
B. Depth-First –Search
C. Best-First-Search
D. Hill climbing
Answer:- c
2. A* generates an optimal solution under which of the following conditions?
Condition 1: If h(n) is an admissible heuristic and the search space is a tree.
Condition 2: If h(n) is a consistent heuristic and the search space is a graph.
A. A* never generate an optimal solution.
B. Condition 1 only
C. Condition 2 only
D. Both Condition 1 and Condition 2
Answer:- d
3. Which of the following statements are true for inadmissible heuristics?
A. Overestimate the cost.
B. Breaks optimality by trapping good plans on the fringe.
C. Slow down search by assigning a lower cost to a bad plan.
D. Underestimate the cost.
Answer:- a, b, c
4. A solution to a CSP is a complete assignment that satisfies all the constraints. Which of the two are true for a solution to a CSP?
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
5. Arc Consistency is a method of constraint propagation. Which of the following statements are correct for arc consistency?
A. Arc consistency is substantially stronger than forward checking.
B. Detects failure early!
C. Arc consistency is one of the most powerful propagation techniques for binary constraints.
D. Provides a fast method of constraint propagation.
Answer:- b, c, b
6. ____________ is a backtracking algorithm for CSP that is aware of the underlying constraint graph; determines where to jump back to, based on the actual conflict that it has recorded.
A. Chronological Backtracking
B. Conflict-directed Backjumping
C. Arc Consistency
D. Plain Backtracking
Answer:- b
For Q.7 – Q.10
Consider an instance of the 8 puzzle problem. We are given the state on the left side and we want to transform it into the state on the right. A problem with fewer restrictions on the actions is called a relaxed problem. The cost of an optimal solution to a relaxed problem is an admissible heuristic for the original problem. Consider two admissible heuristics Hamming Distance h1 and Manhattan Distance h2.
7. Compute the Hamming Distance of the start state for the given instance h1(start).
Answer:- 5
8. Compute the Manhattan Distance of the start state for the given instance h2(start).
Answer:- 6
9. Find the number of moves required to reach the goal from the start using the Manhattan Distance heuristics.
Answer:- 6
10. How many states were required to be examined for reaching the goal using the Manhattan Distance heuristics?
Answer:- 5
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