NPTEL Artificial Intelligence Search Methods For Problem Solving

NPTEL Artificial Intelligence Search Methods For Problem Solving Assignment 3 Answers 2023

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NPTEL Artificial Intelligence Search Methods For Problem Solving Assignment

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.

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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.

Final score = Assignment score + Unproctored programming exam score + Proctored Exam 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
YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF ASSIGNMENT SCORE >=10/25 AND
UNPROCTORED PROGRAMMING EXAM SCORE >=10/25 AND PROCTORED EXAM SCORE >= 20/50. 
If any one of the 3 criteria is not met, you will not be eligible for the certificate even if the Final score >= 40/100. 

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Group2

8. The heuristic values for S, A, B, C, D, and E are: 2, 3, 2, 1, 3, 2.

9. The two nodes in level 2 that will be placed in the beam are: 00010, 00100.

10. The heuristic values of the two nodes in level 3 are: 1, 2.

11. The variable assignment that makes the SAT formula true is: 00010.

Group3

12. A heuristic function computes an estimate of the distance between a node and its nearest goal node.

13. Breadth First Search

14. Working with the 2-City-Exchange or the 2-Edge-Exchange operator means that one is using a Perturbative method to solve the TSP.

15. The Iterated Hill Climbing algorithm can work for both planning and configuration problems.

16. The Hill Climbing algorithm may run into a local optimum because the MoveGen (neighbourhood) function does not connect the node to a better neighbour.

17. The Variable Neighbourhood Descent is an extension of Hill Climbing.

18. Simulated Annealing generates one random neighbour and may possibly move to it whether it is better or worse.

19. Simulated Annealing begins with more random moves and gradually decreases randomness.

20. In stochastic local search, random walk introduces exploration in search.

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