The MiniMax algorithm is a fundamental decision-making strategy in game theory and artificial intelligence, widely applied in two-player zero-sum games like...
MiniMax is a recursive decision-making algorithm used in game theory and AI for two-player zero-sum games, designed to find the optimal move by minimizing the maximum possible loss for the player.
It explores the game tree, assigning scores to terminal states. It then propagates these scores upwards, with the maximizing player choosing moves leading to the highest score, and the minimizing player choosing moves leading to the lowest score, assuming optimal play from both sides.
MiniMax is ideal for deterministic, perfect-information, two-player zero-sum games, where both players know all moves and the outcome, such as chess, checkers, tic-tac-toe, and Othello.
Its primary limitation is computational complexity. The number of possible moves and game states grows exponentially, making it impractical for games with very large game trees without optimizations like alpha-beta pruning.
Alpha-beta pruning is an optimization technique for the MiniMax algorithm that reduces the number of nodes evaluated in the search tree without altering the final result, significantly improving its efficiency by cutting off branches that cannot influence the final decision.