Minimax
Minimax is a decision-making algorithm used in artificial intelligence and game theory for two-player zero-sum games, where one player's gain is the other's loss. It recursively evaluates possible moves by assuming both players play optimally, maximizing the minimum gain for the maximizing player and minimizing the maximum gain for the minimizing player. This algorithm is foundational for creating AI opponents in games like chess, tic-tac-toe, and checkers.
Developers should learn Minimax when building AI for turn-based games or decision-making systems where adversarial scenarios exist, as it provides a robust strategy for optimal play under perfect information. It is particularly useful in game development, robotics planning, and competitive AI applications, helping to simulate intelligent opponents by exploring game trees to find the best move.