The answer should be drawn out like the example above, and all steps 1-28 need to be filled out in the answer.

Adversarlal Games Objectives: Understanding and mastering Minimax algorithm and Alpha-Beta pruning algorithm in adversarial games Alpha-Beta pruning is not actually a new algorithm, rather an optimization technique for minimax algorithm. It reduces the computation time by a huge factor. This allows us to search much faster and even go into deeper levels in the game tree. It cuts off branches in the game tree which need not be searched because there already exists a better move available. It is called Alpha-Beta pruning because it passes 2 extra parameters in the minimax function, namely alpha and beta. Following the following the pseudo code for minimax function, please answer the questions 1 and 2 , assuming that we call the function minimax(0, 0, true, -INFINITY, +INFINITY) for the first time. function minimax(node, depth, isMaximizingPlayer, alpha, beta): if node is a leaf node : return value of the node if isMaximizingPlayer : bestVal = INFINITY for each child node : value =minimax( node, depth +1, false, alpha, beta) bestVal =max( bestVal, value ) alpha =max( alpha, bestVal) if beta =+ INFINITY for each child node : value =minimax( node, depth +1, true, alpha, beta) bestVal =min ( bestVal, vallue) beta =min( beta, bestVal) if beta MAX and MIN, assuming that you are MAX, please figure out the input parameters, isMaximizingPlayer, alpha, beta, (arrow down) and returned values (arrow up) in steps (1) - (28) according to the pseudo code. minimax(0,0, true, -INFINITY, +INFINITY) MAX MIN (5) 3 5