Min-max search is a recursive algorithm that is used to find the optimal move for a player in a game. The algorithm works by recursively exploring all possible moves from the current state of the game, and then selecting the move that leads to the best outcome for the player.
The min-max algorithm works by alternating between two players, called the MAX player and the MIN player. The MAX player is trying to maximize their score, while the MIN player is trying to minimize their score. The algorithm starts at the root of the game tree, and then recursively explores all possible moves from that node.
For each node, the algorithm calculates the value of the node from the perspective of the player currently to move. If the current player is the MAX player, then the value of the node is the maximum value of all of the child nodes. If the current player is the MIN player, then the value of the node is the minimum value of all of the child nodes.
The algorithm continues to explore the game tree until it reaches a leaf node, which is a node that has no children. The value of a leaf node is simply the score of the game state represented by that node.
The min-max algorithm is a very powerful algorithm, but it can be computationally expensive to run. This is because the algorithm has to explore all possible moves from the current state of the game, which can lead to a very large game tree.
There are a number of variations of the min-max algorithm that can be used to improve its performance. One variation is alpha-beta pruning, which is a technique that can be used to eliminate some of the branches of the game tree that are not worth exploring. Another variation is iterative deepening, which is a technique that can be used to improve the accuracy of the min-max algorithm by exploring the game tree to a deeper depth.
Min-max search is a powerful algorithm that is used in a variety of games, including chess, checkers, and Go. The algorithm is also used in other applications, such as artificial intelligence and robotics.
Advantages of Min-Max Search
Min-max search is a very efficient algorithm for finding the optimal move in a game.
Min-max search can be used to solve a wide variety of games.
Min-max search is a very well-understood algorithm, and there are a number of different variations that can be used to improve its performance.
Disadvantages of Min-Max Search
Min-max search can be computationally expensive to run, especially for large games.
Min-max search can be very slow to converge, especially for games with a large number of possible moves.
Min-max search can be very difficult to implement, especially for complex games.