Minimax
Sign in to saveAlso known as minmax, MM
Minimax (sometimes Minmax, MM or saddle point) 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 u
~20 min read
Article
18 sectionsContents
- Game theory
- In general games
- In zero-sum games
- Example
- Maximin
- In repeated games
- Combinatorial game theory
- Pseudocode
- Example
- For individual decisions
- In the face of uncertainty
- Criterion in statistical decision theory
- Non-probabilistic decision theory
- Minimax in democracy
- Maximin in philosophy
- See also
- References
- External links
Minimax (sometimes Minmax, MM or saddle point) 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.
== Game theory ==