The correct answer is: D. All of the mentioned
An evaluation function is a function that assigns a value to a board position, indicating how good it is for the player whose turn it is. Transposition is a technique used in game tree search to avoid re-evaluating the same board position multiple times. Alpha-beta pruning is a technique used in game tree search to reduce the number of nodes that need to be evaluated.
All of these techniques are used to calculate the feasibility of a whole game tree. The evaluation function is used to estimate the value of a board position, and the transposition table and alpha-beta pruning are used to avoid re-evaluating the same board position multiple times.
Here is a more detailed explanation of each technique:
- Evaluation function
An evaluation function is a function that assigns a value to a board position, indicating how good it is for the player whose turn it is. The value of a board position is usually a number between 0 and 1, where 0 indicates a loss for the player whose turn it is, 1 indicates a win for the player whose turn it is, and 0.5 indicates a draw.
The evaluation function is used to guide the search algorithm. The search algorithm starts at the root node of the game tree and evaluates the value of that node. If the value of the root node is greater than 0.5, then the search algorithm expands the child nodes of the root node that are favorable to the player whose turn it is. If the value of the root node is less than 0.5, then the search algorithm expands the child nodes of the root node that are favorable to the opponent.
The evaluation function is a key component of any game tree search algorithm. A good evaluation function can make a big difference in the performance of the search algorithm.
- Transposition table
A transposition table is a data structure that is used to store the results of previous evaluations. The transposition table is used to avoid re-evaluating the same board position multiple times.
When the search algorithm evaluates a board position, it checks the transposition table to see if the value of the board position has already been evaluated. If the value
of the board position has already been evaluated, then the search algorithm uses the value from the transposition table instead of re-evaluating the board position.The transposition table can significantly improve the performance of the search algorithm. By avoiding re-evaluating the same board position multiple times, the search algorithm can focus on evaluating new board positions.
- Alpha-beta pruning
Alpha-beta pruning is a technique used to reduce the number of nodes that need to be evaluated in a game tree search. Alpha-beta pruning works by pruning branches of the game tree that cannot lead to a better outcome than the current best outcome.
The search algorithm starts at the root node of the game tree and evaluates the value of that node. If the value of the root node is greater than or equal to the alpha value, then the search algorithm prunes the child nodes of the root node. If the value of the root node is less than or equal to the beta value, then the search algorithm prunes the child nodes of the root node.
Alpha-beta pruning can significantly reduce the number of nodes that need to be evaluated in a game tree search. By pruning branches of the game tree that cannot lead to a better outcome than the current best outcome, the search algorithm can focus on evaluating the branches of the game tree that are more likely to lead to a better outcome.