# Fundamentals of Artificial Intelligence Assignment 2 Answers 2022

NPTEL Fundamentals of Artificial Intelligence Assignment 2 Answers 2022:- In this post, We have provided answers of the NPTEL Fundamentals of Artificial Intelligence Assignment 2. We provided answers here only for reference. Plz, do your assignment at your own knowledge.

## NPTEL Fundamentals of Artificial Intelligence Assignment 2 Answers 2022 [July-Dec]

Q1. The A* search algorithm is based on ___________

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 gra
ph.

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`

## About Fundamentals Of Artificial Intelligence

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.

COURSE LAYOUT

• Week 1:    AI and Problem Solving by Search
• Week 2:    Problem Solving by Search
• Week 3:    Problem Solving by Search
• Week 4:    Knowledge Representation and Reasoning
• Week 5:    Knowledge Representation and Reasoning
• Week 6:    Knowledge Representation and Reasoning
• Week 7:    Reasoning under Uncertainty
• Week 8:    Planning
• Week 9:    Planning and Decision Making
• Week 10:  Machine Learning
• Week 11:  Machine Learning
• Week 12:  Machine Learning

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

`Note:- We do not claim 100% Surety of Answers, we urge to your assignment at you own Knowledge.`

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