minimax

简明释义

英[ˈmɪnɪˌmæks]美[ˈmɪnəˌmæks]

n. 极大极小；使对方得点减到最低以使自己得最高分的战略

复 数 m i n i m a x e s

英英释义

单词用法

同义词

反义词

例句

1.In the present paper some theorems for variational inequalities and minimax inequality are obtained in hyperconvex metric spaces.

摘要文章给出了超凸度量空间中的一些变分不等式定理和极大极小不等式定理。

2.The minimax game tree, of course, cannot be used very well for games in which the computer cannot see the possible moves.

MGT当然不能预测所有计算机游戏的可能步骤。

3.Minimax is a Linux distribution that fits entirely in an initrd image!

Minimax是一个完全封装在initrd映像文件中的Linux发行版！

4.As applications, a fixed point theorem, a maximal element theorem, a coincidence theorem, some minimax inequalities are proved in FC-space.

作为应用，一不动点定理，一极大元定理，一重合点定理和一些极小极大不等式被证明。

5.With a full minimax tree, the computer could look ahead for each move to determine the best possible move.

对一棵完整的极大极小树来说，计算机能够向前遍历每一步，直到找到最佳步骤为止。

6.So, much of the classical approach to AI consists of things like minimax trees, preprogrammed databases, and prewritten code.

因此，许多古典方法认为人工智能应该由类似极大极小树，预编程数据库和预编代码组成。

7.When developing AI for board games, programmers often rely on the minimax algorithm to evaluate potential moves.

在开发棋盘游戏的人工智能时，程序员通常依赖于最小最大算法来评估潜在的移动。

8.The chess algorithm implements a minimax approach to determine the best move.

国际象棋算法采用最小最大方法来确定最佳移动。

9.In game theory, the strategy known as minimax is used to minimize the possible loss for a worst-case scenario.

在博弈论中，称为最小最大的策略用于最小化最坏情况下可能的损失。

10.In a two-player game, each player aims to maximize their score while minimizing the opponent's score using the minimax strategy.

在双人游戏中，每个玩家都旨在通过使用最小最大策略来最大化自己的得分，同时最小化对手的得分。

11.The minimax principle can help in decision-making by providing a clear framework for evaluating risks.

通过提供一个清晰的风险评估框架，最小最大原则可以帮助决策。

作文

In the realm of game theory and decision-making, the concept of minimax plays a crucial role in determining optimal strategies. The term minimax refers to a decision rule used for minimizing the possible loss while maximizing the potential gain in competitive situations. This strategy is particularly prevalent in zero-sum games, where one player's gain is equivalent to another player's loss. Understanding the minimax principle can provide valuable insights into various fields, including economics, politics, and artificial intelligence.To illustrate the application of minimax, let us consider a simple two-player game where each player has a set of strategies to choose from. Player A wants to maximize their score, while Player B aims to minimize it. In this scenario, Player A must anticipate Player B's moves and select their strategy accordingly. By applying the minimax approach, Player A evaluates the worst-case scenario for each of their strategies, effectively minimizing the maximum possible loss.For instance, imagine a game of chess where each player must decide their next move. A chess player might use the minimax strategy to analyze all possible future positions, considering how their opponent would respond to each move. By evaluating these positions, the player can choose the move that leads to the most favorable outcome while minimizing the risk of a poor result.The minimax principle is not limited to traditional games; it also finds applications in economic models and political strategy. In economics, firms often compete against one another, and understanding the minimax strategy can help them make informed decisions about pricing, production, and market entry. By anticipating competitors' actions and potential market reactions, firms can develop strategies that protect their interests while maximizing profits.Moreover, in the context of artificial intelligence, the minimax algorithm is widely used in the development of intelligent agents that can play games against humans or other machines. The algorithm systematically explores all possible moves and outcomes, allowing the AI to choose the best possible action based on the minimax strategy. This has led to the creation of highly sophisticated game-playing programs that can challenge even the best human players.However, it is essential to recognize the limitations of the minimax approach. In complex games with numerous possible moves and outcomes, calculating the minimax value can become computationally expensive and time-consuming. As a result, researchers have developed various heuristics and optimizations, such as alpha-beta pruning, to enhance the efficiency of the minimax algorithm.In conclusion, the minimax principle is a fundamental concept in decision-making processes across various disciplines. By focusing on minimizing potential losses while maximizing gains, individuals and organizations can devise strategies that lead to favorable outcomes in competitive environments. Whether in games, economics, or artificial intelligence, the application of the minimax strategy demonstrates the importance of careful planning and strategic thinking. As we continue to explore the complexities of decision-making, the minimax principle will undoubtedly remain a vital tool for achieving success.

在博弈论和决策制定的领域中，minimax 概念在确定最佳策略方面起着至关重要的作用。术语 minimax 指的是一种决策规则，用于在竞争情况下最小化可能的损失，同时最大化潜在的获益。该策略在零和游戏中特别普遍，在这种游戏中，一个玩家的收益等于另一个玩家的损失。理解 minimax 原则可以为经济学、政治学和人工智能等多个领域提供有价值的见解。为了说明 minimax 的应用，让我们考虑一个简单的双人游戏，其中每个玩家都有一组策略可供选择。玩家 A 想要最大化他们的分数，而玩家 B 则旨在最小化它。在这种情况下，玩家 A 必须预见到玩家 B 的举动，并相应地选择他们的策略。通过应用 minimax 方法，玩家 A 评估每个策略的最坏情况，从而有效地最小化最大可能的损失。例如，想象一个国际象棋游戏，每个玩家都必须决定他们的下一步行动。一个国际象棋玩家可能会使用 minimax 策略来分析所有可能的未来局面，考虑对手将如何应对每一步。通过评估这些位置，玩家可以选择导致最有利结果的举动，同时最小化糟糕结果的风险。minimax 原则并不仅限于传统游戏；它还在经济模型和政治战略中找到了应用。在经济学中，企业之间往往存在竞争，理解 minimax 策略可以帮助它们在定价、生产和市场进入方面做出明智的决策。通过预见竞争对手的行为和潜在的市场反应，企业可以制定保护自身利益的策略，同时最大化利润。此外，在人工智能的背景下，minimax 算法被广泛用于开发能够与人类或其他机器进行游戏的智能代理。该算法系统地探索所有可能的移动和结果，使 AI 能够根据 minimax 策略选择最佳行动。这导致了高度复杂的游戏程序的创建，甚至可以挑战最优秀的人类玩家。然而，必须认识到 minimax 方法的局限性。在具有众多可能移动和结果的复杂游戏中，计算 minimax 值可能变得计算成本高昂且耗时。因此，研究人员开发了各种启发式方法和优化技术，例如 alpha-beta 剪枝，以提高 minimax 算法的效率。总之，minimax 原则是各个学科决策过程中的基本概念。通过专注于最小化潜在损失的同时最大化收益，个人和组织可以制定出在竞争环境中获得有利结果的策略。无论是在游戏、经济学还是人工智能中，minimax 策略的应用展示了仔细规划和战略思考的重要性。随着我们继续探索决策的复杂性，minimax 原则无疑将继续成为取得成功的重要工具。