minimaxmaximaxperiaatteet

minimaxmaximaxperiaatteet, often shortened to just minimax, is a decision-making algorithm used in game theory and artificial intelligence. It is designed to find the optimal move for a player in a two-player, zero-sum game, assuming the opponent also plays optimally. The core idea is to minimize the maximum possible loss the player can suffer.

In a game, a player's goal is to maximize their own score while simultaneously minimizing the opponent's score. The minimax algorithm explores the game tree, which represents all possible sequences of moves. It assigns a value to each terminal position (end of the game) and then works backward. For a maximizing player (whose turn it is to maximize their score), the value of a non-terminal position is the maximum of the values of its successor positions. Conversely, for a minimizing player (whose turn it is to minimize the opponent's score), the value of a non-terminal position is the minimum of the values of its successor positions.

This recursive process continues until the root of the game tree is reached, which represents the current state of the game. The algorithm then selects the move that leads to the position with the highest value for the maximizing player. While conceptually simple, the minimax algorithm can be computationally expensive for games with large game trees, as it requires exploring a vast number of possibilities. Techniques like alpha-beta pruning are often employed to optimize its performance by cutting off branches of the game tree that are guaranteed not to lead to an optimal solution.