# 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

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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?

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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):

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:

v̂ π (A) =
v̂ π (B) =
v̂ π (C) =
v̂ π (D) =
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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Breadth-first search is complete even if zero step costs are allowed.
Greedy best first search guarantees both completeness and optimality.
A* is of no use in robotics because percepts, states, and actions are continuous.
A simple hill-climbing search can never reach global optimum of a function with multiple local optima.

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).

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.

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.

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.

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.
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.
When we use alpha-beta pruning, the trade-off is that we may obtain a suboptimal solution.
Using alpha-beta pruning does not affect the optimality of the solution.
The ordering of nodes on the same level of the tree does not affect the runtime of the algorithm.
Alpha-beta pruning uses recursion to send back up min, max, alpha, and beta values from leaf nodes.
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.
There could exist a game tree in which MAX can do even by better using a suboptimal strategy against a suboptimal MIN.

For the following situation, should one use adversarial search?

A zero-sum game with one or more opponents.

For the following situation, should one use adversarial search?

Finding the right classification for a group of images.

For the following situation, should one use adversarial search?

A single-player puzzle game, such as Sudoku.

For the following situation, should one use adversarial search?

Playing chess against oneself.

If the CSP has no solution, it is guaranteed that enforcement of arc consistency resulted in at least one domain being empty.
If the CSP has a solution, then after enforcing arc consistency, you can directly read off the solution from resulting domains.
In general, to determine whether the CSP has a solution, enforcing arc consistency alone is not sufficient; backtracking may be required.

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.

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.

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.

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.

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.

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.

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.

Even when using arc consistency, backtracking might be needed to solve a CSP.
Even when using forward checking, backtracking might be needed to solve a CSP.
Solving a CSP problem means finding the assignment(s) that satisfy all constraints.

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.

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.

Is the following statement True or False?

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

Is the following statement True or False?

P(a|b∧a) = 1

Is the following statement True or False?

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

Is the following statement True or False?

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

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.)

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).

If the only difference between two MDPs is the value of the discount factor then they must have the same optimal policy.
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.
When running value iteration, if the policy (the greedy policy with respect to the values) has converged, the values must have converged as well.
If one is using value iteration and the values have converged, the policy must have converged as well.
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.
“Q-values” are determined by immediate expected reward plus the best utility from the next state onwards.

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)

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)

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)

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)

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.

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.

Posting submission...