# NPTEL An Introduction to Artificial Intelligence Assignment 10 Answers

NPTEL An Introduction to Artificial Intelligence Assignment 10 Answers 2022:- All the Answers are provided here to help the students as a reference, You must submit your assignment with your own knowledge

## What is An Introduction to Artificial Intelligence?

An Introduction to Artificial Intelligence by IIT Delhi course introduces a 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.

## NPTEL An Introduction to Artificial Intelligence Assignment 10 Answers 2022:-

Q1. If the utility of money m for an agent is log(m), then that agent is a

a. Risk-prone agent
b. Risk-averse agent
c. Risk-neutral agent

Q2. Consider an MDP having three states A, B, C. Any transition that ends up in states A, B or C yields a reward of -2, -4, 0 respectively. C is the terminal state. Actions x, y are possible in the states A and B with transition probabilities shown in the diagram.

Assume that the discount factor is 1. We wish to compute the optimal policy in this setting using policy iteration. Assume that we have a randomly initialized policy 𝜋0 If the policy 𝜋0 is to execute x in state A, and y in state B, then what is the value of 𝑉𝜋0 (𝐴)?

Q3. Consider the next iteration of the policy iteration algorithm and let the resultant policy be.𝜋1. What are 𝜋1 (𝐴) and 𝜋1(𝐵)?

• x,y
• y,x
• x,x
• y,y

Q4. Let 𝜋 be the optimal policy discovered using policy iteration, then what is the value of V*(A)?

Q5. Which of the following quantities is(are) necessarily finite if an MDP has finite states and finite actions per state?

• The number of possible value functions
• The number of possible deterministic policies
• Values of each state if discount factor is 1
• Values of each state if the discount factor is < 1

Q6. If we perform value iteration, then what is the value of V*(s2) at the end of the second round? (Round your answer to two decimal places)

Q7. Consider a deterministic policy π such that π(s0) = a00, π(s1) = a1, π(s2) = a20, π(s3) = a3, π(s4) = a41. What will be the value of state s0 under this policy? Round your answer to two decimal places.

Q8. The MDP has which of the following properties?

Q9. In general, Value Iteration will converge with fewer number of iterations compared to Policy Iteration.

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Q10. Why is the discount factor used in an MDP formulation?