**NPTEL An Introduction to Artificial Intelligence Assignment 4 Answers 2022:-** All the Answers provided here to help the students as a reference, You must submit your assignment at your own knowledge

## What is An Introduction to Artificial Intelligence?

An Introduction to Artificial Intelligence by **IIT Delhi** course introduces the variety of concepts in the field of artificial intelligence. It discusses the philosophy of AI, and how to model a new problem as an AI problem. It describes a variety of models such as search, logic, Bayes nets, and MDPs, which can be used to model a new problem. It also teaches many first algorithms to solve each formulation. The course prepares a student to take a variety of focused, advanced courses in various subfields of AI.

## CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of the 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 THE 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.**

An Introduction to Artificial Intelligence | Answers |

Assignment 1 | Click Here |

Assignment 2 | Click Here |

Assignment 3 | Click Here |

Assignment 4 | Click Here |

Assignment 5 | Click Here |

Assignment 6 | Click Here |

Assignment 7 | NA |

Assignment 8 | NA |

Assignment 9 | NA |

Assignment 10 | NA |

Assignment 11 | NA |

Assignment 12 | NA |

## NPTEL An Introduction to Artificial Intelligence Assignment 4 Answers 2022:-

**Q1. Which of the following algorithms is asymptotically complete?**

- Random sampling of states
- Local beam search
- Random walk
- Hill climbing with random restarts

**Answer:- (1) & (3)**

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**Q2. Genetic algorithms are said to jump from one hill to another. Which of the following is responsible for such behavior?**

- Mutation
- Cross over
- Fitness function
- Natural Selection

**Answer:- 2. Cross over**

**Q3. Mona was doing a Hill Climbing procedure to solve a problem. She observed that her procedure often gets stuck in plateaus.. Which of the following additions to the procedure would you recommend to her?**

- Allow indefinite sideway moves
- Allow sideway moves but upto a certain limit (say, 100)
- Keep a tabu list of recently visited nodes
- Perform a bfs to find the next node with a better objective function when the state is stuck in a plateau

**Answer:- 2. Allow sideway moves but upto a certain limit (say, 100)**

**Q4. Which of the following is/are true about local search algorithms?**

- They are guaranteed to always terminate with a correct solution
- They are highly memory efficient compared to tree/graph search methods
- They can only solve a certain variety of optimization functions
- They don’t make any assumptions on the structure of optimization function

**Answer:- (2), (4)**

**Q5. Fischl wishes to solve the 8-queens problem using Hill climbing with random restarts and with no sideway moves allowed. It is known that the probability of a successful run of the hill climb algorithm on this problem is 0.14. On average, how many restarts should she expect? (Round off your answer to the closest integer)**

**Answer:- 7**

**Q6. The famous FF planner uses which of the following algorithms?**

- Heuristic Search
- Enforced hill climbing
- Iterative Deepening Search
- Greedy hill Climbing with random restart

**Answer:- 2. Enforced hill climbing**

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**Q7. Gradient descent is guaranteed to converge for strictly convex functions assuming the step size λ is sufficiently small. Assume that we have the function y = x ^{2}. For what values of λ, will gradient descent converge?**

- 0.1
- 0.5
- 1.0
- 2.5

**Answer:- For Answer Click Here**

**For Questions 8 and 9**

In Simulated Annealing, generally, the temperature is reduced from a positive value to a low value in successive iterations. Assume the following 3 step temperature schedule:

[20, 10, 0]

Assume that we have three states s1, s2, and s3 such that V(s1) = 5, V(s2) = 0, and V(s3) = 10. Successors of a state are chosen uniformly randomly. The successor states are defined as: next(s1) = {s2}, next(s2) = {s1, s3}, next(s3) = {s2}. Assume that s3 is the start state.

Answer the next 2 questions based on this setting.

**Q8. After the first iteration of simulated annealing, what is the probability that the current state would be s _{2}. Round the answer to three digits after the decimal point.**

**Answer:- 0.607** (We Are Not Sure, Mark at your own Risk)

**Q9. What is the probability that when the algorithm ends, we are at the state with the highest value?Round the answer to three digits after the decimal point.**

**Answer:- 0.552** (We Are Not Sure, Mark at your own Risk)

**Q10. What are the difference(s) between Simulated Annealing (SA) and Genetic Algorithms (GA)**

- GA maintains multiple candidate solutions while SA does not.
- SA is used for minimization problems while GA is used for maximization problems
- SA has no parameters to set whereas GA requires you to set multiple parameters such as crossover rate.
- GA will always converge to an optimal solution faster than SA on any given problem.

**Answer:- 1. GA maintains multiple candidate solutions while SA does not.**

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**NPTEL An Introduction to Artificial Intelligence Assignment 4 Answers 2022:-** All the Answers provided here to help the students as a reference, You must submit your assignment at your own knowledge.

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