![]() ![]() ![]() Here, the Max agent tries to maximize the score and Min agent tries to minimize the score. Let us assume we have the above game tree formed by two agents (max and min) playing a hypothetical game. Let us understand this with the help of an example. We will try to use α and β to prune our search tree by skipping choices which can’t possibly give us a better solution. ![]() ![]() These values will be passed down to recursion calls via arguments. α is the best score achievable by the max player so far and β is the best score achievable by the min player so far. We will maintain two additional parameters α and β. Please read my post on Minimax algorithm if you haven’t already.Īlpha-beta pruning is based on the Branch and bound algorithm design paradigm, where we will generate uppermost and lowermost possible values to our optimal solution and using them, discard any decision which cannot possibly yield a better solution than the one we have so far. Hello people, in this post we will try to improve the performance of our Minimax algorithm by applying Alpha-Beta Pruning. ![]()
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