In the alpha-beta pruning algorithm, alpha and beta are initially assigned the worst possible values for the maximizing and minimizing player, respectively.

1. Alpha is the best value that the maximizer currently can guarantee at that level or above. It is initially set to negative infinity (-∞), as maximizer wants to get the maximum possible value.

2. Beta is the best value that the minimizer currently can guarantee at that level or above. It is initially set to positive infinity (∞), as minimizer wants to get the minimum possible value.

As the algorithm progresses, these values are updated: alpha is updated when we are at a maximizing level, and beta is updated when we are at a minimizing level. The alpha value will keep increasing and the beta value will keep decreasing as the algorithm progresses.

The pruning occurs when the minimum score that the minimizing player is assured of becomes less than the maximum score that the maximizing player is assured of (i.e., beta

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In the alpha-beta pruning algorithm, alpha and beta are initially assigned the worst possible values for the maximizing and minimizing player, respectively.

1. Alpha is the best value that the maximizer currently can guarantee at that level or above. It is initially set to negative infinity (-∞), as maximizer wants to get the maximum possible value.

2. Beta is the best value that the minimizer currently can guarantee at that level or above. It is initially set to positive infinity (∞), as minimizer wants to get the minimum possible value.

As the algorithm progresses, these values are updated: alpha is updated when we are at a maximizing level, and beta is updated when we are at a minimizing level. The alpha value will keep increasing and the beta value will keep decreasing as the algorithm progresses.

The pruning occurs when the minimum score that the minimizing player is assured of becomes less than the maximum score that the maximizing player is assured of (i.e., beta < alpha). This means that the maximizing player will choose a move that gives a score greater than beta, so the minimizing player will never choose this branch, as they have a better option. Hence, the remaining nodes in this branch can be pruned.

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In the alpha-beta pruning algorithm, alpha and beta are initially assigned the worst possible values for the maximizing and minimizing player, respectively.

1. Alpha is the best value that the maximizer currently can guarantee at that level or above. It is initially set to negative infinity (-∞), as maximizer wants to get the maximum possible value.

2. Beta is the best value that the minimizer currently can guarantee at that level or above. It is initially set to positive infinity (∞), as minimizer wants to get the minimum possible value.

As the algorithm progresses, these values are updated: alpha is updated when we are at a maximizing level, and beta is updated when we are at a minimizing level. The alpha value will keep increasing and the beta value will keep decreasing as the algorithm progresses.

The pruning occurs when the minimum score that the minimizing player is assured of becomes less than the maximum score that the maximizing player is assured of (i.e., beta < alpha). This means that the maximizing player will choose a move that gives a score greater than beta, so the minimizing player will never choose this branch, as they have a better option. Hence, the remaining nodes in this branch can be pruned.

Which value is assigned to alpha and beta in the alpha-beta pruning?ans.Beta = maxAlpha = maxBeta = minBoth Alpha = max & Beta = min Previous Marked for Review Next

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