The minimax algorithm is used in game theory and artificial intelligence to find the optimal moves for a player, assuming that the opponent is also playing optimally. Here's how you can implement the minimax algorithm in MATLAB:
main.m1406 chars40 lines
In the code above, gameState
represents the current state of the game, depth
represents the current depth of the search tree, and isMaximizingPlayer
is a boolean indicating if it's the maximizing player's turn. The getPossibleMoves
, applyMove
, evaluate
, and isGameOver
functions should be replaced with functions that implement the rules of the game. The output of the minimax
function is the best move and the score of that move.
Note that the code above assumes that the maximizing player is attempting to maximize the score, and the minimizing player is attempting to minimize the score. If you're working with a game where the goal is to minimize some value (for example, the number of moves required to solve a puzzle), you'll need to modify the evaluate
function to return negative scores for better states.
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