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– ## HW2.docx **## HW2.docx** This problem exercises the basic concepts of game playing, using tic-tac-toe as an example. We define Xn as the number of rows, columns, or diagonals with exactly n X's and no O's. Similarly, On is the number of rows, columns, or diagonals with exactly n O's. The utilit…
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– ## HW2.docx
**## HW2.docx**
This problem exercises the basic concepts of game playing, using tic-tac-toe as an example. We define Xn as the number of rows, columns, or diagonals with exactly n X's and no O's. Similarly, On is the number of rows, columns, or diagonals with exactly n O's. The utility function assigns +1 to any position with X3 = 1 and -1 to any position with O3 = 1. All other terminal positions have utility 0. For nonterminal positions, we use a linear evaluation function defined as Eval(s) = X1(s) – O1(s).
A. Approximately how many possible games of tic-tac-toe are there?
B. Show the game tree starting from an empty board down to depth 2 (i.e., one X and one O on the board), taking symmetry into account.
C. Mark on your tree the evaluations of all the positions at depth 2.
D. Using the minimax algorithm, mark on your tree the backed-up values for the positions at depths 1 and 0, and use those values to choose the best starting move.
E. Mark the nodes at depth 2 that would not be evaluated if alpha-beta pruning were applied, assuming the nodes are generated in the optimal order for alpha-beta pruning.
**## HW2.docx**
This problem exercises the basic concepts of game playing, using tic-tac-toe as an example. We define Xn as the number of rows, columns, or diagonals with exactly n X's and no O's. Similarly, On is the number of rows, columns, or diagonals with exactly n O's. The utility function assigns +1 to any position with X3 = 1 and -1 to any position with O3 = 1. All other terminal positions have utility 0. For nonterminal positions, we use a linear evaluation function defined as Eval(s) = X1(s) – O1(s).
A. Approximately how many possible games of tic-tac-toe are there?
B. Show the game tree starting from an empty board down to depth 2 (i.e., one X and one O on the board), taking symmetry into account.
C. Mark on your tree the evaluations of all the positions at depth 2.
D. Using the minimax algorithm, mark on your tree the backed-up values for the positions at depths 1 and 0, and use those values to choose the best starting move.
E. Mark the nodes at depth 2 that would not be evaluated if alpha-beta pruning were applied, assuming the nodes are generated in the optimal order for alpha-beta pruning.
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