Exam

The exam consists of five topic questions, one from each topic discussed in this course, and a set of true-false-questions that can be from any of the topics. Notice that some of the questions are easier to solve using pen and paper.

Keep an eye on the time, and remember to submit all of your answers before the time runs out. If the time runs out before submitting your answers, the grader does not give you points from that answer.

Keep an eye also on the number of submissions. For some of the questions, you can submit multiple answers. The grader will immediately tell you if your answer was correct.

Notice that the short story needed for the N-gram question can also be downloaded here. The Story of An Hour - Kate Chopin.txt

Question 1

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 5 be the start node and 7 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 2

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 4 be the start node and 5 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 3

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 2 be the start node and 6 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 4

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 8 be the start node and 2 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 5

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 3 be the start node and 7 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 6

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 2 be the start node and 5 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 7

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 6 be the start node and 2 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 8

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 7 be the start node and 6 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 9

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 6 be the start node and 4 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 10

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 7 be the start node and 5 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 11

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 6 be the start node and 1 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 12

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 6 be the start node and 2 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 13

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 7 be the start node and 0 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 14

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 2 be the start node and 8 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 15

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 7 be the start node and 5 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 16

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 6 be the start node and 7 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 17

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 5 be the start node and 7 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 18

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 0 be the start node and 7 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 19

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 2 be the start node and 5 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 20

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 6 be the start node and 8 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 21

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 7 be the start node and 3 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 22

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 5 be the start node and 7 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 23

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 4 be the start node and 3 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 24

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 2 be the start node and 3 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 25

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 3 be the start node and 6 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 26

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 6 be the start node and 3 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 27

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 3 be the start node and 7 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 28

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 8 be the start node and 7 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 29

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 7 be the start node and 1 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 30

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 2 be the start node and 8 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 31

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 5 be the start node and 7 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 32

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 8 be the start node and 4 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 33

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 2 be the start node and 7 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 34

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 8 be the start node and 6 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 35

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 6 be the start node and 8 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 36

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 7 be the start node and 8 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 37

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 2 be the start node and 8 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 38

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 7 be the start node and 8 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 39

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 4 be the start node and 8 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 40

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 5 be the start node and 7 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 41

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 7 be the start node and 0 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 42

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 1 be the start node and 6 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 43

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 6 be the start node and 7 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 44

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 1 be the start node and 4 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 45

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 8 be the start node and 6 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 46

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 6 be the start node and 8 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 47

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 7 be the start node and 6 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 48

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 4 be the start node and 1 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 49

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 6 be the start node and 1 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 50

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 4 be the start node and 8 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 51

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 5 be the start node and 7 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 52

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 4 be the start node and 7 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 53

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 8 be the start node and 4 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 54

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 6 be the start node and 1 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 55

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 6 be the start node and 5 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 56

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 5 be the start node and 4 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 57

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 4 be the start node and 1 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 58

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 8 be the start node and 6 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 59

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 7 be the start node and 6 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 60

Consider the following graph G. It consist of 9 nodes labeled with numbers as well as 12 edges connecting the nodes. Each edge is labeled with its cost. Let 6 be the start node and 8 be the goal node. Run Dijkstra’s algorithm on G. What is the total cost of the path from start to goal?

Question 1

Consider the minimax tree above with the following values: A:-2, B:12, C:9, D:5, E:12, F:7, G:14, H:-1, I:-4, J:-4, K:5, L:-4, M:12, N:-1, O:6, P:6 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 2

Consider the minimax tree above with the following values: A:0, B:14, C:1, D:-2, E:11, F:-2, G:9, H:-5, I:6, J:-4, K:-3, L:2, M:8, N:1, O:8, P:10 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 3

Consider the minimax tree above with the following values: A:9, B:6, C:10, D:13, E:8, F:14, G:3, H:14, I:6, J:6, K:10, L:12, M:-4, N:3, O:5, P:-1 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 4

Consider the minimax tree above with the following values: A:1, B:2, C:2, D:-3, E:7, F:-2, G:9, H:-3, I:10, J:5, K:14, L:10, M:-1, N:3, O:7, P:7 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 5

Consider the minimax tree above with the following values: A:7, B:9, C:2, D:2, E:7, F:2, G:13, H:-3, I:8, J:11, K:10, L:9, M:1, N:6, O:5, P:2 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 6

Consider the minimax tree above with the following values: A:9, B:-1, C:-1, D:0, E:13, F:-5, G:4, H:6, I:8, J:0, K:1, L:7, M:-1, N:-3, O:-2, P:12 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 7

Consider the minimax tree above with the following values: A:5, B:12, C:10, D:11, E:-4, F:-2, G:-1, H:9, I:10, J:4, K:6, L:2, M:8, N:-4, O:-1, P:0 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 8

Consider the minimax tree above with the following values: A:-1, B:6, C:11, D:1, E:12, F:2, G:6, H:1, I:1, J:9, K:13, L:8, M:4, N:5, O:9, P:-4 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 9

Consider the minimax tree above with the following values: A:12, B:11, C:-2, D:8, E:-3, F:-2, G:-4, H:8, I:3, J:9, K:13, L:9, M:-3, N:-2, O:14, P:11 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 10

Consider the minimax tree above with the following values: A:-4, B:3, C:11, D:-5, E:-1, F:5, G:-1, H:11, I:13, J:6, K:-5, L:-2, M:5, N:7, O:7, P:6 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 11

Consider the minimax tree above with the following values: A:-1, B:-5, C:4, D:-2, E:3, F:0, G:11, H:4, I:6, J:14, K:1, L:2, M:-3, N:9, O:5, P:3 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 12

Consider the minimax tree above with the following values: A:9, B:6, C:14, D:8, E:8, F:3, G:7, H:0, I:7, J:2, K:9, L:14, M:7, N:3, O:11, P:2 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 13

Consider the minimax tree above with the following values: A:14, B:-5, C:5, D:11, E:3, F:13, G:11, H:6, I:10, J:1, K:5, L:6, M:0, N:11, O:-3, P:4 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 14

Consider the minimax tree above with the following values: A:10, B:13, C:6, D:13, E:7, F:6, G:-3, H:0, I:3, J:-5, K:3, L:14, M:7, N:5, O:-2, P:10 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 15

Consider the minimax tree above with the following values: A:12, B:5, C:7, D:13, E:5, F:8, G:0, H:12, I:1, J:4, K:-2, L:4, M:1, N:-2, O:14, P:-4 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 16

Consider the minimax tree above with the following values: A:-4, B:13, C:10, D:-1, E:7, F:8, G:-3, H:2, I:4, J:1, K:6, L:14, M:-4, N:-5, O:8, P:-1 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 17

Consider the minimax tree above with the following values: A:9, B:1, C:2, D:4, E:10, F:10, G:6, H:-2, I:-4, J:-4, K:3, L:10, M:-2, N:-5, O:-1, P:12 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 18

Consider the minimax tree above with the following values: A:9, B:11, C:6, D:6, E:6, F:0, G:3, H:11, I:2, J:0, K:-5, L:9, M:-1, N:13, O:-4, P:9 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 19

Consider the minimax tree above with the following values: A:-2, B:5, C:11, D:9, E:-2, F:-3, G:12, H:-4, I:-2, J:11, K:5, L:-2, M:-2, N:6, O:-1, P:9 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 20

Consider the minimax tree above with the following values: A:-5, B:9, C:10, D:0, E:13, F:-3, G:-3, H:2, I:10, J:13, K:9, L:5, M:-4, N:9, O:1, P:2 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 21

Consider the minimax tree above with the following values: A:1, B:-2, C:8, D:12, E:8, F:1, G:-3, H:2, I:9, J:-5, K:1, L:2, M:-1, N:1, O:7, P:9 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 22

Consider the minimax tree above with the following values: A:7, B:2, C:-5, D:-2, E:7, F:14, G:14, H:-5, I:3, J:-1, K:8, L:12, M:1, N:10, O:1, P:14 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 23

Consider the minimax tree above with the following values: A:8, B:-1, C:-1, D:4, E:7, F:-4, G:10, H:9, I:0, J:1, K:-1, L:0, M:11, N:-1, O:1, P:-5 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 24

Consider the minimax tree above with the following values: A:8, B:1, C:13, D:-2, E:11, F:-5, G:-3, H:14, I:8, J:-4, K:-5, L:7, M:1, N:11, O:-4, P:14 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 25

Consider the minimax tree above with the following values: A:-5, B:13, C:-4, D:5, E:-5, F:-1, G:13, H:1, I:10, J:7, K:-5, L:-3, M:11, N:4, O:7, P:1 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 26

Consider the minimax tree above with the following values: A:10, B:1, C:4, D:6, E:2, F:8, G:-1, H:4, I:13, J:7, K:13, L:5, M:3, N:3, O:9, P:0 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 27

Consider the minimax tree above with the following values: A:5, B:0, C:6, D:-4, E:-2, F:-3, G:0, H:14, I:-1, J:8, K:6, L:-5, M:6, N:-5, O:8, P:4 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 28

Consider the minimax tree above with the following values: A:12, B:-4, C:-3, D:-4, E:12, F:8, G:0, H:7, I:12, J:4, K:2, L:-3, M:10, N:0, O:2, P:-4 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 29

Consider the minimax tree above with the following values: A:7, B:3, C:7, D:14, E:-2, F:11, G:4, H:1, I:-3, J:1, K:10, L:11, M:2, N:-5, O:14, P:0 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 30

Consider the minimax tree above with the following values: A:11, B:-2, C:8, D:-3, E:-3, F:11, G:5, H:1, I:-2, J:-2, K:9, L:-2, M:2, N:13, O:-4, P:7 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 31

Consider the minimax tree above with the following values: A:4, B:5, C:6, D:7, E:3, F:14, G:2, H:10, I:-5, J:12, K:12, L:14, M:7, N:8, O:-2, P:-5 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 32

Consider the minimax tree above with the following values: A:-5, B:0, C:9, D:-3, E:10, F:-1, G:-5, H:7, I:14, J:9, K:3, L:9, M:9, N:-4, O:1, P:-4 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 33

Consider the minimax tree above with the following values: A:0, B:4, C:8, D:3, E:-2, F:7, G:9, H:10, I:8, J:0, K:14, L:10, M:-2, N:-2, O:13, P:11 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 34

Consider the minimax tree above with the following values: A:14, B:3, C:5, D:4, E:-4, F:1, G:0, H:8, I:1, J:4, K:-3, L:11, M:0, N:2, O:11, P:-5 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 35

Consider the minimax tree above with the following values: A:-4, B:8, C:12, D:8, E:11, F:8, G:6, H:12, I:8, J:14, K:5, L:13, M:11, N:0, O:6, P:2 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 36

Consider the minimax tree above with the following values: A:12, B:1, C:0, D:8, E:1, F:1, G:-2, H:3, I:1, J:2, K:8, L:8, M:6, N:-2, O:12, P:6 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 37

Consider the minimax tree above with the following values: A:5, B:13, C:6, D:-1, E:1, F:3, G:3, H:-2, I:4, J:-3, K:4, L:-1, M:2, N:1, O:2, P:-5 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 38

Consider the minimax tree above with the following values: A:11, B:-2, C:6, D:0, E:-2, F:12, G:5, H:11, I:14, J:3, K:5, L:-4, M:7, N:4, O:11, P:-4 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 39

Consider the minimax tree above with the following values: A:3, B:0, C:-1, D:-1, E:13, F:-1, G:-1, H:11, I:3, J:-1, K:11, L:-4, M:9, N:4, O:8, P:9 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 40

Consider the minimax tree above with the following values: A:0, B:10, C:1, D:9, E:0, F:-3, G:-3, H:10, I:-2, J:11, K:4, L:1, M:9, N:5, O:3, P:-3 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 41

Consider the minimax tree above with the following values: A:14, B:-3, C:2, D:-4, E:4, F:-2, G:5, H:8, I:7, J:8, K:14, L:8, M:13, N:10, O:-3, P:-5 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 42

Consider the minimax tree above with the following values: A:8, B:-5, C:14, D:9, E:1, F:1, G:7, H:10, I:3, J:-3, K:2, L:8, M:12, N:7, O:-4, P:0 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 43

Consider the minimax tree above with the following values: A:5, B:2, C:-2, D:1, E:11, F:13, G:-4, H:13, I:14, J:9, K:6, L:6, M:4, N:10, O:12, P:13 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 44

Consider the minimax tree above with the following values: A:2, B:13, C:-2, D:-5, E:1, F:2, G:0, H:1, I:-4, J:6, K:-3, L:3, M:0, N:-3, O:5, P:9 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 45

Consider the minimax tree above with the following values: A:11, B:10, C:12, D:8, E:10, F:-4, G:9, H:10, I:-3, J:5, K:9, L:-5, M:-1, N:13, O:5, P:5 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 46

Consider the minimax tree above with the following values: A:10, B:-4, C:4, D:3, E:-4, F:-2, G:11, H:2, I:3, J:8, K:11, L:5, M:5, N:9, O:2, P:6 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 47

Consider the minimax tree above with the following values: A:9, B:11, C:10, D:5, E:0, F:8, G:1, H:-2, I:-3, J:8, K:-5, L:3, M:-1, N:8, O:9, P:-1 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 48

Consider the minimax tree above with the following values: A:11, B:12, C:10, D:-5, E:7, F:4, G:6, H:1, I:-1, J:9, K:2, L:12, M:2, N:14, O:5, P:4 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 49

Consider the minimax tree above with the following values: A:2, B:2, C:2, D:-3, E:12, F:5, G:0, H:3, I:5, J:-2, K:-4, L:5, M:14, N:-5, O:3, P:4 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 50

Consider the minimax tree above with the following values: A:4, B:-3, C:-5, D:12, E:-1, F:-3, G:-4, H:1, I:-4, J:10, K:-2, L:6, M:5, N:-5, O:1, P:-2 Suppose two optimal players, Max and Min, play a game. They take turns trying to maximize and minimize the utility. If Max is the root, what is the minimax value of the root?

Question 1

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 2

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 3

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 4

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 5

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 6

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 7

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 8

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 9

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 10

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 11

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 12

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 13

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 14

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 15

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 16

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 17

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 18

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 19

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 20

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 21

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 22

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 23

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 24

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 25

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 26

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 27

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 28

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 29

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 30

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 31

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 32

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 33

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 34

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 35

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 36

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 37

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 38

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 39

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 40

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 41

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 42

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 43

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 44

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 45

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 46

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 47

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 48

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 49

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

Question 50

The table below contains the full joint probability distribution related to the occurrence of toothaches.

Using these values, calculate P(cavity|toothache ∪ catch) (round all answers to 3 decimal places and use a period as a decimal separator):

What are the estimates for the following quantities as obtained by direct evaluation:

Question 1

v̂ _π (A) =

Question 2

v̂ _π (B) =

Question 3

v̂ _π (C) =

Question 4

v̂ _π (D) =

Question 5

v̂ _π (E) =

Notice that the short story needed for the N-gram question can be downloaded here. The Story of An Hour - Kate Chopin.txt

Question 1

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘her husband’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 157 and submit the remainder.

Question 2

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘it was’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 61 and submit the remainder.

Question 3

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘her sister’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 73 and submit the remainder.

Question 4

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘had been’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 137 and submit the remainder.

Question 5

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘brently mallard’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 101 and submit the remainder.

Question 6

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘he had’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 59 and submit the remainder.

Question 7

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘she did?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 127 and submit the remainder.

Question 8

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘did not’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 79 and submit the remainder.

Question 9

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘its significance’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 127 and submit the remainder.

Question 10

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘she would’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 73 and submit the remainder.

Question 11

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘would have’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 73 and submit the remainder.

Question 12

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘no one’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 173 and submit the remainder.

Question 13

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘open window’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 197 and submit the remainder.

Question 14

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘her body’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 107 and submit the remainder.

Question 15

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘that were’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 193 and submit the remainder.

Question 16

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘some one’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 127 and submit the remainder.

Question 17

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘one was’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 61 and submit the remainder.

Question 18

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘faintly countless’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 113 and submit the remainder.

Question 19

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘patches blue’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 79 and submit the remainder.

Question 20

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘blue sky’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 71 and submit the remainder.

Question 21

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘that had’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 67 and submit the remainder.

Question 22

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘with her’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 59 and submit the remainder.

Question 23

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘she was’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 167 and submit the remainder.

Question 24

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘there was’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 67 and submit the remainder.

Question 25

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘her eyes’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 131 and submit the remainder.

Question 26

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘her she’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 59 and submit the remainder.

Question 27

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘but she’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 191 and submit the remainder.

Question 28

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘when she’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 79 and submit the remainder.

Question 29

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘free free’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 89 and submit the remainder.

Question 30

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘keen bright’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 179 and submit the remainder.

Question 31

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘joy that’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 167 and submit the remainder.

Question 32

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘she saw’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 127 and submit the remainder.

Question 33

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘that would’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 139 and submit the remainder.

Question 34

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘there would’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 179 and submit the remainder.

Question 35

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘would be’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 59 and submit the remainder.

Question 36

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘be no’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 137 and submit the remainder.

Question 37

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘during those’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 101 and submit the remainder.

Question 38

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘she had’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 113 and submit the remainder.

Question 39

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘door with’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 67 and submit the remainder.

Question 40

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘open door’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 89 and submit the remainder.

Question 41

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘that life’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 61 and submit the remainder.

Question 42

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘life might’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 107 and submit the remainder.

Question 43

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘might be’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 167 and submit the remainder.

Question 44

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word bi-gram language model, what is the probability of the phrase ‘be long’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 191 and submit the remainder.

Question 45

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word tri-gram language model, what is the probability of the phrase ‘she did not’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 127 and submit the remainder.

Question 46

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word tri-gram language model, what is the probability of the phrase ‘with paralyzed inability’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 113 and submit the remainder.

Question 47

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word tri-gram language model, what is the probability of the phrase ‘some one was’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 97 and submit the remainder.

Question 48

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word tri-gram language model, what is the probability of the phrase ‘patches blue sky’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 89 and submit the remainder.

Question 49

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word tri-gram language model, what is the probability of the phrase ‘was dull stare’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 67 and submit the remainder.

Question 50

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word tri-gram language model, what is the probability of the phrase ‘her she was’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 53 and submit the remainder.

Question 51

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word tri-gram language model, what is the probability of the phrase ‘there would be’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 131 and submit the remainder.

Question 52

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word tri-gram language model, what is the probability of the phrase ‘would be no’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 197 and submit the remainder.

Question 53

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word tri-gram language model, what is the probability of the phrase ‘that life might’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 79 and submit the remainder.

Question 54

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word tri-gram language model, what is the probability of the phrase ‘life might be’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 59 and submit the remainder.

Question 55

Just above of this quiz, there is a file that contains a short story.

As preprocessing, you should remove all capitalization, special characters, and numbers from the text. In addition, remove the following common words (called “stopwords”): [a, an, and, as, at, for, from, in, into, of, on, or, the, to]

After tokenization, remove also tokens with length < 2. We perform this step because if e.g. you have in the text words like “sister’s”, tokenization will result in meaningless tokens like “s” (Note that this might lead to omitting words such as the personal pronoun “I” which could be undesirable in practice, but for this exercise it is okay).

Using a word tri-gram language model, what is the probability of the phrase ‘might be long’?

Remove the leading zeroes from your answer, and consider the three most significant digits of the result as an integer (e.g. if your answer is 0.00010587, consider only 105 from it). Divide that integer with 83 and submit the remainder.

Question 1

Breadth-first search is complete even if zero step costs are allowed.

True

False

Question 2

Greedy best first search guarantees both completeness and optimality.

True

False

Question 3

A* is of no use in robotics because percepts, states, and actions are continuous.

True

False

Question 4

A simple hill-climbing search can never reach global optimum of a function with multiple local optima.

True

False

Question 5

Player MAX and player MIN are playing a zero-sum game with a finite number of possible moves. MAX calculates the minimax value of the root to be M. You may assume that at every turn, each player has at least 2 possible actions. You may also assume that a different sequence of moves will always lead to a different score (i.e., no two terminal nodes have the same score). Is the following statement True or False?

Assume MIN is playing sub-optimally at every turn, but MAX does not know this. The outcome of the game could be larger than M (i.e. better for MAX).

True

False

Question 6

Player MAX and player MIN are playing a zero-sum game with a finite number of possible moves. MAX calculates the minimax value of the root to be M. You may assume that at every turn, each player has at least 2 possible actions. You may also assume that a different sequence of moves will always lead to a different score (i.e., no two terminal nodes have the same score). Is the following statement True or False?

Assume MIN is playing sub-optimally at every turn. If MAX plays according to the minimax strategy, the outcome of the game could be less than M.

True

False

Question 7

Player MAX and player MIN are playing a zero-sum game with a finite number of possible moves. MAX calculates the minimax value of the root to be M. You may assume that at every turn, each player has at least 2 possible actions. You may also assume that a different sequence of moves will always lead to a different score (i.e., no two terminal nodes have the same score). Is the following statement True or False?

Assume MIN is playing sub-optimally at every turn. MAX following the minimax policy will guarantee a better outcome than M.

True

False

Question 8

Player MAX and player MIN are playing a zero-sum game with a finite number of possible moves. MAX calculates the minimax value of the root to be M. You may assume that at every turn, each player has at least 2 possible actions. You may also assume that a different sequence of moves will always lead to a different score (i.e., no two terminal nodes have the same score). Is the following statement True or False?

Assume MIN is playing sub-optimally at every turn, and MAX knows exactly how MIN will play. There exists a policy for MAX to guarantee a better outcome than M.

True

False

Question 9

In a fully observable, turn-taking, zero-sum game between two perfectly rational players, it does not help the first player to know what strategy the second player is using - that is, what move the second player will make, given the first player’s move.

True

False

Question 10

In a partially observable, turn-taking, zero-sum game between two perfectly rational players, it does not help the first player to know what move the second player will make, given the first player’s move.

True

False

Question 11

When we use alpha-beta pruning, the trade-off is that we may obtain a suboptimal solution.

True

False

Question 12

Using alpha-beta pruning does not affect the optimality of the solution.

True

False

Question 13

The ordering of nodes on the same level of the tree does not affect the runtime of the algorithm.

True

False

Question 14

Alpha-beta pruning uses recursion to send back up min, max, alpha, and beta values from leaf nodes.

True

False

Question 15

For every game tree, the utility obtained by MAX using minimax decisions against a suboptimal MIN will be never be lower than the utility obtained playing against an optimal MIN.

True

False

Question 16

There could exist a game tree in which MAX can do even by better using a suboptimal strategy against a suboptimal MIN.

True

False

Question 17

For the following situation, should one use adversarial search?

A zero-sum game with one or more opponents.

Yes

No

Question 18

For the following situation, should one use adversarial search?

Finding the right classification for a group of images.

Yes

No

Question 19

For the following situation, should one use adversarial search?

A single-player puzzle game, such as Sudoku.

Yes

No

Question 20

For the following situation, should one use adversarial search?

Playing chess against oneself.

Yes

No

Question 21

If the CSP has no solution, it is guaranteed that enforcement of arc consistency resulted in at least one domain being empty.

True

False

Question 22

If the CSP has a solution, then after enforcing arc consistency, you can directly read off the solution from resulting domains.

True

False

Question 23

In general, to determine whether the CSP has a solution, enforcing arc consistency alone is not sufficient; backtracking may be required.

True

False

Question 24

We are given a CSP with only binary constraints. Assume we run backtracking search with arc consistency as follows. Initially, when presented with the CSP, one round of arc consistency is enforced. This first round of arc consistency will typically result in variables having pruned domains. Then we start a backtracking search using the pruned domains. In this backtracking search we use filtering through enforcing arc consistency after every assignment in the search.

Is the following statement True or False?

If after a run of arc consistency during the backtracking search we end up with the filtered domains of all of the not yet assigned variables being empty, this means the CSP has no solution.

True

False

Question 25

We are given a CSP with only binary constraints. Assume we run backtracking search with arc consistency as follows. Initially, when presented with the CSP, one round of arc consistency is enforced. This first round of arc consistency will typically result in variables having pruned domains. Then we start a backtracking search using the pruned domains. In this backtracking search we use filtering through enforcing arc consistency after every assignment in the search.

Is the following statement True or False?

If after a run of arc consistency during the backtracking search we end up with the filtered domain of one of the not yet assigned variables being empty, this means the CSP has no solution.

True

False

Question 26

We are given a CSP with only binary constraints. Assume we run backtracking search with arc consistency as follows. Initially, when presented with the CSP, one round of arc consistency is enforced. This first round of arc consistency will typically result in variables having pruned domains. Then we start a backtracking search using the pruned domains. In this backtracking search we use filtering through enforcing arc consistency after every assignment in the search.

Is the following statement True or False?

If after a run of arc consistency during the backtracking search we end up with the filtered domains of all of the not yet assigned variables being empty, this means the search should backtrack because this particular branch in the search tree has no solution.

True

False

Question 27

We are given a CSP with only binary constraints. Assume we run backtracking search with arc consistency as follows. Initially, when presented with the CSP, one round of arc consistency is enforced. This first round of arc consistency will typically result in variables having pruned domains. Then we start a backtracking search using the pruned domains. In this backtracking search we use filtering through enforcing arc consistency after every assignment in the search.

Is the following statement True or False?

If after a run of arc consistency during the backtracking search we end up with the filtered domain of one of the not yet assigned variables being empty, this means the search should backtrack because this particular branch in the search tree has no solution.

True

False

Question 28

We are given a CSP with only binary constraints. Assume we run backtracking search with arc consistency as follows. Initially, when presented with the CSP, one round of arc consistency is enforced. This first round of arc consistency will typically result in variables having pruned domains. Then we start a backtracking search using the pruned domains. In this backtracking search we use filtering through enforcing arc consistency after every assignment in the search.

Is the following statement True or False?

If after a run of arc consistency during the backtracking search we end up with the filtered domains of all of the not yet assigned variables each having exactly one value left, this means we have found a solution.

True

False

Question 29

We are given a CSP with only binary constraints. Assume we run backtracking search with arc consistency as follows. Initially, when presented with the CSP, one round of arc consistency is enforced. This first round of arc consistency will typically result in variables having pruned domains. Then we start a backtracking search using the pruned domains. In this backtracking search we use filtering through enforcing arc consistency after every assignment in the search.

Is the following statement True or False?

If after a run of arc consistency during the backtracking search we end up with the filtered domains of all of the not yet assigned variables each having more than one value left, this means we have found a whole space of solutions and we can just pick any combination of values still left in the domains and that will be a solution.

True

False

Question 30

We are given a CSP with only binary constraints. Assume we run backtracking search with arc consistency as follows. Initially, when presented with the CSP, one round of arc consistency is enforced. This first round of arc consistency will typically result in variables having pruned domains. Then we start a backtracking search using the pruned domains. In this backtracking search we use filtering through enforcing arc consistency after every assignment in the search.

Is the following statement True or False?

If after a run of arc consistency during the backtracking search we end up with the filtered domains of all of the not yet assigned variables each having more than one value left, this means we can’t know yet whether there is a solution somewhere further down this branch of the tree, and search has to continue down this branch to determine this.

True

False

Question 31

Even when using arc consistency, backtracking might be needed to solve a CSP.

True

False

Question 32

Even when using forward checking, backtracking might be needed to solve a CSP.

True

False

Question 33

Solving a CSP problem means finding the assignment(s) that satisfy all constraints.

True

False

Question 34

Consider the Bayesian network in the figure above. In the Conditional Probability Tables (CPTs), the letters B, E, A, J and M stand for Burglary, Earthquake, Alarm, JohnCalls, and MaryCalls, respectively.

True or False: If no evidence is observed, Burglary and Earthquake are independent.

True

False

Question 35

Consider the Bayesian network in the figure above. In the Conditional Probability Tables (CPTs), the letters B, E, A, J and M stand for Burglary, Earthquake, Alarm, JohnCalls, and MaryCalls, respectively.

True or False: If we observer Alarm = true, Burglary and Earthquake are independent.

True

False

Question 36

Is the following statement True or False?

If P(a|b,c) = P(b|a,c), then P(a|c) = P(b|c)

True

False

Question 37

Is the following statement True or False?

P(a|b∧a) = 1

True

False

Question 38

Is the following statement True or False?

If P(a|b,c) = P(a), then P(b|c) = P(b)

True

False

Question 39

Is the following statement True or False?

P(a|b) = P(a), then P(a|b,c) = P(a|c)

True

False

Question 40

Suppose you are a witness to a nighttime hit-and-run accident involving a taxi in Athens. All taxis in Athens are blue or green. You swear, under oath, that the taxi was blue. Extensive testing shows that, under the dim lighting conditions, discrimination between blue and green is 75% reliable.

It is possible to calculate the most likely color for the taxi given the current information. (Hint: distinguish carefully between the proposition that the taxi is blue and the proposition that it appears blue.)

True

False

Question 41

You are given three statements.

1. P(X,Y|Z) = P(X|Z)P(Y|Z)

2. P(X|Y,Z) = P(X|Z)

3. P(Y|X,Z) = P(Y|Z)

True or False: statement 1 is equivalent to (statement 2 ∧ statement 3).

True

False

Question 42

If the only difference between two MDPs is the value of the discount factor then they must have the same optimal policy.

True

False

Question 43

For an infinite horizon MDP with a finite number of states and actions and with a discount factor γ that satisfies 0 < γ < 1, value iteration is guaranteed to converge.

True

False

Question 44

When running value iteration, if the policy (the greedy policy with respect to the values) has converged, the values must have converged as well.

True

False

Question 45

If one is using value iteration and the values have converged, the policy must have converged as well.

True

False

Question 46

For an infinite horizon MDP with a finite number of states and actions and with a discount factor γ that satisfies 0 < γ < 1, policy iteration is guaranteed to converge.

True

False

Question 47

“Q-values” are determined by immediate expected reward plus the best utility from the next state onwards.

True

False

Question 48

Note: One round of policy iteration = performing policy evaluation followed by performing policy improvement.

Is the following statement True or False?

It is guaranteed that ∀s ∈ S : V _{π James} (s) ≥ V _{π Alvin} (s)

True

False

Question 49

Note: One round of policy iteration = performing policy evaluation followed by performing policy improvement.

Is the following statement True or False?

It is guaranteed that ∀s ∈ S : V _{π Michael} (s) ≥ V _{π Alvin} (s)

True

False

Question 50

Note: One round of policy iteration = performing policy evaluation followed by performing policy improvement.

Is the following statement True or False?

It is guaranteed that ∀s ∈ S : V _{π Michael} (s) > V _{π John} (s)

True

False

Question 51

Note: One round of policy iteration = performing policy evaluation followed by performing policy improvement.

Is the following statement True or False?

It is guaranteed that ∀s ∈ S : V _{π James} (s) > V _{π John} (s)

True

False

Question 52

Is the following statement about value iteration True or False? We assume the MDP has a finite number of actions and states, and that the discount factor satisfies 0 < γ < 1.

Value iteration is guaranteed to converge.

True

False

Question 53

Is the following statement about value iteration True or False? We assume the MDP has a finite number of actions and states, and that the discount factor satisfies 0 < γ < 1.

Value iteration will converge to the same vector values (V*) no matter what values we use to initialize V.

True

False

Posting submission...