The Minimax algorithm is a key strategy in Artificial Intelligence, especially in game theory. It is used in two-player turn-based games like chess and tic-tac-toe to make optimal decisions. The algorithm simulates all possible moves, assuming the opponent plays optimally. One player tries to maximize the score, while the other tries to minimize it—hence the name “Minimax.”
AI uses this method to evaluate moves by building a game tree and selecting the best possible outcome. The algorithm ensures that the AI never makes a move that could lead to the worst-case scenario. When combined with techniques like Alpha-Beta Pruning, Minimax becomes more efficient.
Used widely in board game AIs, the Minimax algorithm remains a foundational concept in AI decision-making.