Alpha-Beta Pruning in Artificial Intelligence

Alpha-Beta Pruning

• Alpha-beta pruning is a modified version of the minimax algorithm.
• It is an optimization technique for the minimax algorithm.
• This involves two threshold parameter Alpha and beta for future expansion, so it is called alpha-beta pruning. It is also called as Alpha-Beta Algorithm. Alpha-beta pruning can be applied at any depth of a tree, and sometimes it not only prune the tree leaves but also entire sub-tree.
• The two-parameter can be defined as:
• Alpha: The best (highest-value) choice we have found so far at any point along the path of Maximizer. The initial value of alpha is -∞.
• Beta: The best (lowest-value) choice we have found so far at any point along the path of Minimizer. The initial value of beta is +∞.

Condition for Alpha-beta pruning:

The main condition which required for alpha-beta pruning is:α>=β

Key points about alpha-beta pruning:

• The Max player will only update the value of alpha.
• The Min player will only update the value of beta.
• While backtracking the tree, the node values will be passed to upper nodes instead of values of alpha and beta.
• We will only pass the alpha, beta values to the child nodes.

Working of Alpha-Beta Pruning:

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Originally published at https://pywix.blogspot.com.