# Minimax algorithm alpha beta pruning example. Minimax Algorithm with Alpha 2022-12-14

Minimax algorithm alpha beta pruning example Rating: 9,6/10 1200 reviews

The minimax algorithm is a search algorithm used in decision-making and game theory to determine the optimal move for a player, assuming that the other player will also play optimally. It works by considering all possible moves that a player could make, and then calculating the minimum score that the opponent could achieve as a result of each move. The player then chooses the move that maximizes their score while minimizing the opponent's score.

One way to improve the efficiency of the minimax algorithm is through the use of alpha beta pruning. This technique involves maintaining two values, alpha and beta, which represent the minimum and maximum scores that the player can expect, respectively. If at any point during the search, it becomes clear that the current score is worse than the alpha or beta values, the search can be stopped, as the player will not choose a move that results in a worse score.

For example, consider a simple two-player game where the players take turns placing coins on a board, and the player who gets three in a row first wins. The minimax algorithm would search through all possible moves, calculating the minimum score that the opponent could achieve as a result of each move. Using alpha beta pruning, the search could be stopped as soon as it becomes clear that the current score is worse than either the alpha or beta values.

In this example, assume that it is the first player's turn, and that the alpha and beta values are initially set to negative infinity and positive infinity, respectively. The first player considers placing a coin in the top left corner of the board. This results in a score of -1 for the first player and 1 for the second player, as the second player now has a chance to block the first player's three in a row. The first player then considers placing a coin in the top center of the board, resulting in a score of 0 for both players.

At this point, the alpha value is updated to 0, as this is the minimum score that the first player can expect. The first player then considers placing a coin in the top right corner of the board, resulting in a score of 1 for the first player and -1 for the second player. The first player chooses this move, as it results in the highest score while minimizing the opponent's score.

Overall, the minimax algorithm is a powerful tool for decision-making and game theory, and the use of alpha beta pruning can significantly improve its efficiency by allowing the search to be stopped early when it becomes clear that a move will not result in the optimal outcome.

## Minimax search and alpha

White evaluates move A,B, and C and finds the best score is 20 with C. We're looking for the minimum value, in this case. Minimax search Suppose that we assign a value of positive infinity to a leaf state in which we win, negative infinity to states in which the opponent wins, and zero to tie states. Ci â€” iâ€”th alphaâ€”beta pruning. But as we know, the performance measure is the first consideration for any optimal algorithm.

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## Minimax Algorithm with Alpha

This node returns a value of 3. Ever since the advent of Artificial Intelligence AI , game playing has been one of the most interesting applications of AI. However, we have not considered all of blacks counter-moves. Since equal values would not produce a better result, there's not point in exploring that path further. The method used in alpha-beta pruning is that it cutoff the search by exploring less number of nodes. .

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## Minimax Alpha Beta Pruning Sample

Actually, in general the tree is a graph, because there may be more than one way to get to a particular state. . The graph is directed since it does not necessarily mean that we'll be able to move back exactly where we came from in the previous move, e. So it continues the search. At that point, MAX has to choose the highest value.

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## dynamic programming

Therefore, when I examine the second row, I need to figure out what's the worst-case scenario, for me, for each position. Set initial values of alpha and beta as follows: a. Therefore, I need to implement a game, and then create an abstraction that allows the alpha-beta pruning algorithm to work its magic without having to worry about the exact game that we implemented. This answer Thank you. If the layer is min, the score of that node is the lowest score of it's children.

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## Minimax algorithm and alpha

But the maximizer was already guaranteed a value of 5 if he choose B. Therefore, Minimax applies search to a fairly low tree depth aided with appropriate evaluation function. He has the choice of playing m moves. Minimax dummy implementation In order to solidify our knowledge, let's make a dummy implementation of the minimax algorithm. It is defined for all the layers in the tree. But how does he do that? With this approach we lose the certainty in finding the best possible move, but the majority of cases the decision that minimax makes is much better than any human's. Alpha is the minimum value that the max player is guaranteed regardless of what the min player does through another path through the game tree.

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## Minimax with Alpha

So we have to evaluate MAX{3,2} which is 3. Description Aplha-Beta pruning is a optimization technique used in minimax algorithm. Just how big is that number? For example, what if we managed to avoid traversing certain parts of the tree, if we know those parts are irrelevant? Beta: It is the best choice so far for MIN, and it has to be the lowest possible value. ? But you and me are humans, and we are taking a look at the whole drawing all at once. So far, we only applied the minimax algorithm to very small trees. It depends on how well I can do after we reach that position.

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## Minimax Algorithm in Game Theory (Alpha

Note: Alpha-beta pruning technique can be applied to trees of any depth, and it is possible to prune the entire subtrees easily. Trying to implement the algorithm will push you towards understanding, so let's do it. This is also the reason why the evaluation results are easily cacheable since they will be the same each time a position is evaluated. For min nodes, we want to visit the worst child first from our perspective, not the opponent's. I have spent a whole day trying to implement minimax without really understanding it.

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## (DOC) Minimax with Alpha Beta Pruning Good example

For instance, in chess or tic-tac-toe, the outcome is either a win, a loss, or a draw, and these can be represented by the values +1, -1, or 0, respectively. The value of alpha here is -INFINITY and the value of beta is +INFINITY. . For reference, if we compared the mass of an electron 10 -30kg to the mass of the entire known universe 10 50-10 60kg , the ratio would be in order of 10 80-10 90. Let's see how the previous tree will look if we apply alpha-beta method: When the search comes to the first grey area 8 , it'll check the current best with minimum value already explored option along the path for the minimizer, which is at that moment 7. This is what you should get: print minimax tree, True 7 print minimax tree, False 5 Alpha-beta pruning rationale Now that we have a basic understanding of the minimax algorithm, let's introduce the alpha-beta pruning optimisation. It had to be high enough so that the 5 wouldn't stop the algorithm, but it has to be low enough so that the 7 can stop the algorithm! The maximiser node is 5 or more, but we don't want the algorithm to stop.

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