minimax

To build an AI opponent for a two-player board game, you provide a game-state representation, a depth parameter, and an evaluation heuristic, allowing the minimax algorithm to recursively evaluate game states and return the optimal next move.

Minimax reasoning is an adversarial decision-making strategy that recursively explores game states to minimize the opponent's maximum possible payoff, ensuring robust decisions in turn-based planning and worst-case scenarios.

You optimize moves in turn-based planning by applying a minimax evaluation with a defined depth parameter and evaluation heuristic to your game-state representation, returning the best worst-case move.

For minimax move selection to work, you need to provide a simple two-player game-state representation, a search depth parameter, and an evaluation heuristic to measure the worst-case outcomes.

Use minimax over other game-theory or path search approaches when robust decisions are required in two-player, zero-sum games where worst-case outcomes matter and adversarial opponents must be accounted for.

A key limitation of minimax is its reliance on a depth parameter; complex adversarial game states require deeper recursive exploration, increasing computational overhead and demanding a highly accurate evaluation heuristic.