Minimax Algorithm

Imagine you're playing a board game against a smart opponent. The Minimax algorithm is a way for an AI to decide its next move. It works by looking ahead at all possible outcomes of moves for both players. The 'Min' part comes from the idea that your opponent will try to minimize your score or maximize their own. The 'Max' part is you trying to maximize your own score, assuming your opponent is doing their best.

So, the algorithm explores a 'game tree', which is like a branching diagram of all possible moves and counter-moves. It assigns a score to each final possible game state, like winning, losing, or drawing. Then, it works backward from these final states. If it's the AI's turn, it picks the move that leads to the highest score. If it's the opponent's turn, it assumes the opponent will pick the move that leads to the lowest score for the AI.

Let's say you're playing tic-tac-toe. The AI considers a move it could make. Then, it considers all the moves its opponent could make in response. For each of those, it considers its own next moves, and so on. It simulates this until the game ends or a certain depth is reached. Finally, it traces back the best path. The Minimax algorithm is a core component in many AI game players, from chess to Connect Four.

A common limitation is that as games become more complex, the number of possible moves and outcomes grows exponentially. This can make it computationally impossible to look at every single possibility. To solve this, advanced versions like Alpha-Beta Pruning are used, which smartly cut off branches of the game tree that are clearly not going to lead to the best outcome. Despite its simplicity conceptually, Minimax is a foundational stone in game AI.