# Artificial Intelligence: Search Methods For Problem Solving Assignment 4 Answers

NPTEL Artificial Intelligence: Search Methods For Problem Solving Assignment 4 Answers 2022:- In this post, We have provided answers to the Artificial Intelligence: Search Methods For Problem Solving Assignment 4. We provided answers here only for reference. Plz, do your assignment to your own knowledge.

## About NPTEL Artificial Intelligence: Search Methods For Problem Solving

For an autonomous agent to behave in an intelligent manner it must be able to solve problems. This means it should be able to arrive at decisions that transform a given situation into a desired or goal situation. The agent should be able to imagine the consequence of their decisions to be able to identify the ones that work. In this first course on AI, we study a wide variety of search methods that agents can employ for problem-solving.

In a follow-up course – AI: Knowledge Representation and Reasoning – we will go into the details of how an agent can represent its world and reason with what it knows. These two courses should lay a strong foundation for artificial intelligence, which the student can build upon. A third short course – AI: Constraint Satisfaction Problems – presents a slightly different formalism for problem-solving, one in which the search and reasoning processes mentioned above can operate together.

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of the average of the 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 THE AVERAGE ASSIGNMENT SCORE >=10/25 AND YOUR 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.

## NPTEL Artificial Intelligence: Search Methods For Problem Solving Assignment 4 Answers

1. The idea of using population-based methods to solve optimization problems

a. is only to exploit parallel search like in Iterative Hill Climbing
b. is to hope that one of the parallel Simulated Annealing searches will find the solution
c. is to go beyond parallelism and exploit interaction between members of the population
d. is to exploit the diversity in a population of candidates and try mixing up the candidate solutions

`Answer:- b, c, d`

2. Which of the following are true? Mark all correct answers

a. Genetic Algorithms work with a population of problem solving agents
b. Genetic Algorithms work with a population of candidate solutions
c. Ant Colony Optimization works with a population of problem solving agents
d. Ant Colony Optimization works with a population of candidate solutions

`Answer:- a, c, d`

3. Genetic Algorithms work best when

a. there is a large population of diverse candidates
b. there is a large population of similar candidates
c. there is a small population of diverse candidates
d. there is a small population of similar candidates

`Answer:- b`

4. Darwins theory of natural selection can be seen as

a. a process of selecting the best leaders based on their education
b. a process of selecting the best leaders based on their ability to tell lies
c. a process to design life forms that evolve and improve over generations
d. a process where members of a population compete for survival

`Answer:- a, b, c, d`

5. Which one of the following completes the quote from Paul Valery in the context of Genetic Algorithms? “It takes two to invent anything. The one makes up combinations, _________________”

a. the other creates the permutations
b. the other chooses
c. the other sorts the components
d. the other randomizes them

`Answer:- a`

6. What is the role an individual ant plays in the Ant Colony Algorithm?

a. It conveys the messages from the queen to the soldiers
b. It constructs a candidate solution using divide and conquer approach
c. It constructs a candidate solution using a greedy local stochastic search approach
d. It guards the entrance of the ant colony

`Answer:- b`

7. Consider chromosomes made of 5-bit binary strings and a fitness function f(a,b,c,d,e) that is a square of (a+2b+3c+2d+e), where “abcde” is a 5-bit chromosome. An initial population is shown in the table.

`Answer:- 7, 3, 4`

8. In the previous question, the instances selected for crossover are _________.

a. 01010, 01001, 01101, 00110
b. 01010, 01010, 01101, 00110
c. 01010, 01101, 01101, 00110
d. 01010, 01001, 00110, 00110

`Answer:- b`

9. Avoid making guesses, review the various tour representations before answering this question. Select the path representations of the tour.

a. A,D,F,C,B,G,J,H,I,E,A
b. G,B,C,F,D,A,E,I,H,J,G
c. A,D,F,C,B,G,J,H,I,E
d. G,B,C,F,D,A,E,I,H,J

`Answer:- a`

10. Valid adjacency representations of the tour are _________ .

a. D,G,B,F,A,C,J,I,E,H
b. A,D,F,C,B,G,J,H,I,E
c. J,E,B,H,A,D,F,I,G,C
d. E,C,F,A,I,D,B,J,H,G

`Answer:- d`

11. Compute the ordinal representations for the valid path representations of the tour, listed in Q9. Select the correct options.

a. 1,3,4,2,7,3,2,2,2,1
b. 7,2,2,4,2,1,1,2,1,1
c. 1,3,4,2,1,2,4,2,2,1
d. 2,6,8,6,1,4,2,1,1,1

`Answer:- a`

12. Path representations of two tours are given below. Generate two offspring using Partially Mapped Crossover, use the locations from 4 to 7 as the mapping segment.

P1: A,D,F,C,B,G,J,H,I,E
P2: B,G,J,H,A,F,D,C,E,I

`Answer:- A,B,G,I`

13. Path representations of two tours are given below. Generate two offspring using Order Crossover, use locations from 4 to 7 as the mapping segment. In the child tour, place the mapping segment in the middle. For example, C1 = ?,?,?,C,B,G,J,?,?,?

P1: A,D,F,C,B,G,J,H,I,E
P2: B,G,J,H,A,F,D,C,E,I

`Answer:- E,I,H,B,A`

14. Path representations of two tours are given below. Generate the two offspring using Cycle Crossover.

P1: A,D,F,C,B,G,J,H,I,E
P2: B,G,J,H,A,F,D,C,E,I

`Answer:- A,D,F,H`

15. Path representations of two tours are given below. Compute the ordinal representations of the parent tours and use single point crossover (cut in the middle) to generate two offspring.

P1: A,D,F,C,B,G,J,H,I,E
P2: B,G,J,H,A,F,D,C,E,I

`Answer:- C,A,B`

16. The classical single point crossover operator (cut two chromosomes and swap the pieces) used in genetic algorithms can be applied to _________.

a. the path representation
c. the ordinal representation
d. all of the above

`Answer:- d`

17. What is the tour generated by Greedy Heuristic? Use the edge costs given in the table below. Enter the path representation of the tour starting from city A.

`Answer:- F`

18. Starting from city A, what is the tour generated by the Nearest Neighbour Heuristic? Use the edge costs given in the previous question. Enter the path representation of the tour starting from city A.

`Answer:- B`

19. Select the correct options. For a TSP problem with N cities ___________ .

a. every path representation has a unique ordinal representation
b. every tour has a unique ordinal representation
c. every tour has a unique adjacency representation
d. every tour has exactly two adjacency representations

`Answer:- b, c`

20. Given a column of three alive cells in Conway’s Game of Life shown as gray cells in the figure, how will the cellular automaton evolve in the next time step?

a. The three cells will die out.
b. The three cells will remain as they are.
c. The three cells will shift one step to the right.
d. The three cells will rotate forming a horizontal row of three cells.

`Answer:- c`

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