Alpha-beta pruning is an algorithm used in tree searching algorithms to decrease the number of nodes that are evaluated by the search algorithm. It is an extension of the minimax algorithm, which is used in decision making and game theory.

In the alpha-beta pruning algorithm, two values are maintained while executing the algorithm: alpha and beta.

1. Alpha: This is the best (highest-value) choice we have found so far at any point along the path of Maximizer. The initial value of alpha is -∞. So, at the start of the algorithm, alpha is the worst case for Maximizer.

2. Beta: This is the best (lowest-value) choice we have found so far at any point along the path of Minimizer. The initial value of beta is +∞. So, at the start of the algorithm, beta is the worst case for Minimizer.

As the algorithm progresses, these values are updated based on the values returned by the child nodes in the tree. If the value of alpha becomes greater than or equal to beta at any point, the remaining child nodes are not processed as the current node cannot influence the decision of the ancestors. This is the pruning step of the alpha-beta pruning algorithm.

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Alpha-beta pruning is an algorithm used in tree searching algorithms to decrease the number of nodes that are evaluated by the search algorithm. It is an extension of the minimax algorithm, which is used in decision making and game theory.

In the alpha-beta pruning algorithm, two values are maintained while executing the algorithm: alpha and beta.

1. Alpha: This is the best (highest-value) choice we have found so far at any point along the path of Maximizer. The initial value of alpha is -∞. So, at the start of the algorithm, alpha is the worst case for Maximizer.

2. Beta: This is the best (lowest-value) choice we have found so far at any point along the path of Minimizer. The initial value of beta is +∞. So, at the start of the algorithm, beta is the worst case for Minimizer.

As the algorithm progresses, these values are updated based on the values returned by the child nodes in the tree. If the value of alpha becomes greater than or equal to beta at any point, the remaining child nodes are not processed as the current node cannot influence the decision of the ancestors. This is the pruning step of the alpha-beta pruning algorithm.

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Alpha-beta pruning is an algorithm used in tree searching algorithms to decrease the number of nodes that are evaluated by the search algorithm. It is an extension of the minimax algorithm, which is used in decision making and game theory.

In the alpha-beta pruning algorithm, two values are maintained while executing the algorithm: alpha and beta.

1. Alpha: This is the best (highest-value) choice we have found so far at any point along the path of Maximizer. The initial value of alpha is -∞. So, at the start of the algorithm, alpha is the worst case for Maximizer.

2. Beta: This is the best (lowest-value) choice we have found so far at any point along the path of Minimizer. The initial value of beta is +∞. So, at the start of the algorithm, beta is the worst case for Minimizer.

As the algorithm progresses, these values are updated based on the values returned by the child nodes in the tree. If the value of alpha becomes greater than or equal to beta at any point, the remaining child nodes are not processed as the current node cannot influence the decision of the ancestors. This is the pruning step of the alpha-beta pruning algorithm.

Which value is assigned to alpha and beta in the alpha-beta pruning?

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