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Minimax - Wikipedia

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Minimax (sometimes Minmax, MM[1] or saddle point[2]) is a decision rule used in artificial intelligence, decision theory, combinatorial game theory, statistics, and philosophy for minimizing the possible loss for a worst case (maximum loss) scenario. When dealing with gains, it is referred to as "maximin" – to maximize the minimum gain. Originally formulated for several-player zero-sum game theory, covering both the cases where players take alternate moves and those where they make simultaneous moves, it has also been extended to more complex games and to general decision-making in the presence of uncertainty. The maximin value is the highest value that the player can be sure to get without knowing the actions of the other players; equivalently, it is the lowest value the other players can force the player to receive when they know the player's action. Its formal definition is:[3] Where: Calculating the maximin value of a player is done in a worst-case approach: for each possible actio

Minimax - Wikipedia Jump to content From Wikipedia, the free encyclopedia Decision rule used for minimizing the possible loss for a worst-case scenario This article is about the decision theory concept. For other uses, see Minimax (disambiguation) . For the AI company, see MiniMax Group . Minimax (sometimes Minmax , MM [ 1 ] or saddle point [ 2 ] ) is a decision rule used in artificial intelligence , decision theory , combinatorial game theory , statistics , and philosophy for minimizing the possible loss for a worst case ( max imum loss) scenario . When dealing with gains, it is referred to a

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