Minimaxbased

Minimaxbased is a descriptor used in artificial intelligence and decision theory to indicate methods or systems that apply the minimax decision rule to select actions in adversarial environments. Such approaches assume an opponent who acts to maximize the agent’s loss and aim to minimize the worst-case outcome.

Practically, minimax-based methods build a decision tree of possible moves and outcome values, then propagate values backward from leaf evaluations to determine the action that minimizes the maximum possible loss. In games with large state spaces, exact minimax search is impractical, so variants use depth-limited search with heuristic evaluation functions, iterative deepening, and pruning techniques such as alpha-beta pruning to reduce computation.

Common domains include two-player, zero-sum games such as chess, checkers, and tic-tac-toe, where minimax is a natural fit. They are often used as baselines or components in larger systems, including reinforcement-learning agents that incorporate adversarial considerations and planning modules in robotics or automated negotiation.

Limitations include reliance on accurate modeling of the opponent and complete information; computational complexity grows exponentially with depth; performance hinges on the quality of leaf evaluations. For imperfect information or stochastic opponents, expectimax or other models may be more appropriate.

Relationship to related concepts: minimax-based methods are a subset of adversarial search and are closely related to the classic minimax algorithm and alpha-beta pruning. The term is not a formal standard but a descriptive label used in various texts to indicate a minimax-oriented approach.