Minimax is a decision-making algorithm used in game theory and artificial intelligence. It is commonly used in two-player games with perfect information, such as chess or tic-tac-toe. The algorithm aims to determine the optimal move for a player by considering all possible moves and their potential outcomes.
The name "minimax" comes from the two main objectives of the algorithm: minimizing the maximum possible loss. In other words, it assumes that the opponent will make the best move possible, and the player wants to minimize their potential loss in response.
The algorithm works by recursively evaluating the game tree, which represents all possible moves and their outcomes. At each level of the tree, the algorithm alternates between maximizing and minimizing the potential outcomes. It assigns a value to each possible move, representing the desirability of that move for the player.
By considering all possible moves and their outcomes, the minimax algorithm can determine the best move for a player in a game. However, in games with a large number of possible moves or complex game states, the algorithm can become computationally expensive. Various optimizations, such as alpha-beta pruning, can be applied to improve its efficiency.