NPTEL An Introduction To Artificial Intelligence Week 10 Quiz Solution 2023

ABOUT THE COURSE :
The 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 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.

 Ram has the opportunity to make one of 2 bets (say A,B) or invest equally in both bets or make no bets each of which is based on the outcome of a cricket match. The payoffs to Ram on winning/losing each of the bets are as described in the table below:

1. If Ram employs minimax regret to decide in this situation, what action does he take?

(A) Makes bet A

(B) Makes bet B

(C) Invest equally in A and B

(D) Makes no bet

Answer :- A

2. If Ram employs the Hurwicz criterion to decide, for which of the following values of the coefficient of realism does 

Ram choose to not make a bet?

(A)  0.2

(B)  0.5

(C)  0.7

(D)  0.4

Answer :- A & D

3. Assume that an insider tells Ram that he can tell Ram beforehand whether Ram will win or lose a bet. Also assume

 that all bets have an equal likelihood of success and failure. What is the maximum amount of money Ram should be 

willing to pay the agent for this information?

Answer :- 

4. For an MDP of discrete finite state space S and discrete finite action space, what is the memory size of the transition

 function, in the most general case?

(A)  O(|S|^2)

(B)  O(|S|*|A|)

(C)  O(|S|^2|A|)

(D)  O(|S|*|A|^2)

Answer :- B

For Question 5 - 7 :  

Consider the MDP given below for a robot trying to walk.



The MDP has three states: S={Standing,Moving,Fallen} and two actions: moving the robot legs slowly (a) and moving the robot legs aggressively (b), denoted by the colour black and green respectively. The task is to perform policy iteration for the above MDP with discount factor 1.

5. We start with a policy 𝜋(s) = a for all s in S and V 𝜋 (s) = 0 for all s. What is the value of the Fallen state after one 

iteration of bellman update during policy evaluation?

Return the answer as a decimal rounded to 1 decimal place.

Answer :- -0.2

6. Suppose we perform the policy improvement step just after one iteration of bellman update as in Q5, what is the 

updated policy. Write in the order of actions for Standing, Moving and Fallen.

Example, if the policy is 𝜋(Standing) = b, 𝜋(Moving) = b, 𝜋(Fallen) = a, write the answer as bba.

Answer :- aba

7. After one iteration of policy evaluation as in Q5, what is the value of Q(state,action) where state = Moving and

 action = b?

Return the answer as a decimal rounded to 2 decimal places.

Answer :- 1.4

8. If the utility curve of an agent varies as m^2 for money m, then the agent is:

(A)  Risk-prone

(B)  Risk-averse

(C)  Risk-neutral

(D)  Can be any of these

Answer :- B

9. Which of the following statements are true regarding Markov Decision Processes (MDPs)?

(A)  Discount factor is not useful for finite horizon MDPs.

(B)  We assume that the reward and cost models are independent of the previous state transition history, given 

the current state.

(C)  MDPs assume full observability of the environment

(D)  Goal states may have transitions to other states in the MDP

Answer :- B

10.Which of the following are true regarding value and policy iteration?

(A)  Value iteration is guaranteed to converge in a finite number of steps for any value of epsilon and any MDP,

 if the MDP has a fixed point.

(B)  The convergence of policy iteration is dependent on the initial policy.

(C)  Value iteration is generally expected to converge in a lesser number of iterations as compared to policy

iteration.

(D)  In each iteration of policy iteration, value iteration is run as a subroutine, using a fixed policy

Answer :- A,B & C