Are you looking for the Answers to **NPTEL An Introduction to Artificial Intelligence Assignment 3 – IIT Delhi? **This article will help you with the answer to the **Nation**** al Programme on Technology Enhanced Learning (NPTEL)** Course “

**NPTEL An Introduction to Artificial Intelligence**

**Assignment 3**“

## 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.**

**Below you can find the answers for NPTEL An Introduction to Artificial Intelligence Assignment 3**

Assignment No. | Answers |
---|---|

Assignment 1An Introduction to Artificial Intelligence | Click Here |

Assignment 2An Introduction to Artificial Intelligence | Click Here |

Assignment 3An Introduction to Artificial Intelligence | Click Here |

Assignment 4An Introduction to Artificial Intelligence | Click Here |

Assignment 5An Introduction to Artificial Intelligence | Click Here |

Assignment 6An Introduction to Artificial Intelligence | Click Here |

Assignment 7An Introduction to Artificial Intelligence | Click Here |

Assignment 8An Introduction to Artificial Intelligence | Click Here |

## NPTEL An Introduction to Artificial Intelligence Assignment 3 Answers:-

**Q1.** If h1 and h2 are two admissible heuristics, then which of the following are guaranteed to be admissible?

**Answer:-** **a,b,c**

**Q2.** Which of the following evaluation functions would emulate the behavior of greedy best-first search?

**Answer:-b,c**

**Q3.** Which of the following graph search algorithms are guaranteed to be complete and optimal? (Assume positive edge costs)

**Answer:-** **b,d**

**Q4.** Consider the following directed graph,having A as the starting node and G as the goal node, with edge costs as mentioned, and the heuristic values for the nodes are given as – {h(A)=7, h(B)=6, h(C)=5, h(D)=4, h(E)=3, h(F)=3, h(G)=0}:

**Answer:-** **b,d**

???? **Next Week Answers: Assignment 04** ????

**Q5.** With A as the starting node and G as the goal node, we run the IDA* graph search with depths 1,2,3.. . Assume we stop as soon as the goal node is reached. At what depth will we reach the goal node? (Assume A is at depth 0)

**Answer:-** **3**

**Q6.** With A as the starting node and G as the goal node, we run the IDA* graph search with depths 1,2,3.. . Assume we stop as soon as the goal node is reached. What is the optimal path from A to G predicted by IDA*? (Assume A is at depth 0)

**Answer:-** **ADEG**

**Q7.** What is the cost of the optimal solution in the relaxed domain?

**Answer:-** **b**

**Q8.** Is the cost of the optimal solution an admissible heuristic in the original domain? Why?

**Answer:-** **b**

**Q9.** Consider the graph below. A is the starting node and B is the goal state. We perform a depth first search branch and bound algorithm (assume zero heuristic) with upper bound cost 6.5. If there are any ties, then the algorithm chooses to expand the lexicographically smaller node. Also, we stop as soon as we reach the goal. What is the first path predicted by the algorithm?

**Answer:-** **AECB**

**Q10.** Consider the graph below. A is the starting node and B is the goal state. We perform a depth first search branch and bound algorithm (assume zero heuristic) with upper bound cost 6.5. If there are any ties, then the algorithm chooses to expand the lexicographically smaller node. Also, we stop as soon as we reach the goal. Now, suppose I change my upper bound to 7.5. What will be the path predicted in this case?

**Answer:-** **AECB**

**Q11.** Are these true or false?

**Answer:-** **b**

**Q12.** Which of the following will return an optimal solution in case the search graph has negative edge-costs?

**Answer:-** **c**

**Q13.** Consider the graph shown below and answer the next three questions based on it. In the graph, all nodes represent a state. A is the start state, and G is the goal state. The value of the heuristic function for each node is mentioned alongside. For example, the value of the heuristic at E is 1, and at C is 4, etc. Similarly the cost of transitioning from a state to another state is mentioned on the edges. For example, the cost of transitioning from B to D is 3 points.

**Answer:-** **c**

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