Please answer all of the questions below:

1. Below is a part of a minimax tree, where the root represents a max node. Observe carefully and answer the below questions: 4 4 7 1 1 4 3 3 3 7 1 9 4 1 7 4 6 1 9 1 8 1 1 9 0 4 9 . [10]In the Minimax scheme we assume that both the players are playing optimally. This helps us obtain a minimax value for teach node. Suppose now you are playing against a suboptimal player, would the resulting minimax alter in this scenario? Justify your answer. [20] Now suppose that you are aware when your friend will make a suboptimal move, and which move she will make. Can you take advantage of this? In other words, can a suboptimal strategy on your part achieve higher utility than a minimax strategy if such assumptions are made? If so, provide a game tree that demonstrates this behaviour. If not, provide a proof that this is not possible. 2. [10] Give two advantages of Iterative Deepening minimax algorithms over Depth Limited minimax algorithms. 3. [10] Draw a (small) game tree in which the root node has a larger value if expectimax search is used than if minimax is used, or argue why it is not possible. 4. [10] Under what circumstances, player 1 should use minimax search rather than expecti- max search to select a move? Does player 2's strategy influence this decision? 5. [10] Under what circumstances, player 1 should use expectimax search rather than mini- max search to select a move? Does player 2s strategy influence this decision? 1. Below is a part of a minimax tree, where the root represents a max node. Observe carefully and answer the below questions: 4 4 7 1 1 4 3 3 3 7 1 9 4 1 7 4 6 1 9 1 8 1 1 9 0 4 9 . [10]In the Minimax scheme we assume that both the players are playing optimally. This helps us obtain a minimax value for teach node. Suppose now you are playing against a suboptimal player, would the resulting minimax alter in this scenario? Justify your answer. [20] Now suppose that you are aware when your friend will make a suboptimal move, and which move she will make. Can you take advantage of this? In other words, can a suboptimal strategy on your part achieve higher utility than a minimax strategy if such assumptions are made? If so, provide a game tree that demonstrates this behaviour. If not, provide a proof that this is not possible. 2. [10] Give two advantages of Iterative Deepening minimax algorithms over Depth Limited minimax algorithms. 3. [10] Draw a (small) game tree in which the root node has a larger value if expectimax search is used than if minimax is used, or argue why it is not possible. 4. [10] Under what circumstances, player 1 should use minimax search rather than expecti- max search to select a move? Does player 2's strategy influence this decision? 5. [10] Under what circumstances, player 1 should use expectimax search rather than mini- max search to select a move? Does player 2s strategy influence this decision