MINMAX, also known as minimax search or minimax algorithm, is a decision-making strategy used in game theory and artificial intelligence to determine the best possible moves in adversarial situations. In games such as chess, checkers, or tic-tac-toe, where two players take turns making moves with opposing objectives, the MINMAX algorithm aims to minimize the potential loss for the worst-case scenario from the player's perspective.
The term itself derives from the two main components of the algorithm. "MIN" refers to minimizing the opponent's score or outcome, while "MAX" refers to maximizing one's own score or outcome. The algorithm assumes that both players will play optimally, making the best moves to achieve their respective goals.
To use the MINMAX algorithm, a game tree is constructed, representing all possible moves and subsequent positions. The algorithm then systematically evaluates each node of the tree, assigning a value indicating the desirability of that state. This evaluation is performed by recursively applying MIN and MAX functions to alternate levels of the tree.
The MIN function represents the opponent's moves, attempting to minimize the score or outcome, while the MAX function represents the player's own moves, attempting to maximize their score or outcome. The algorithm backtracks through the tree, comparing and updating the values at each level until it reaches the root node. Finally, the best move is chosen based on the value assigned to the root node.
Overall, the MINMAX algorithm provides a systematic and efficient method for determining optimal choices in adversarial situations by considering the worst-case scenarios. It has been widely applied in various areas, including game playing, decision-making, and optimization problems.
