Minimax

Minimax (sometimes Minmax, MM or saddle point) is a decision rule used in <a href="/facts/Artificial_intelligence/lJGauQwX">artificial intelligence</a>, <a href="/facts/Decision_theory/TbXcQX0v">decision theory</a>, <a href="/facts/Game_theory/zkEIj1ya">game theory</a>, <a href="/facts/Statistics/DNPsKGYU">statistics</a>, and <a href="/facts/Philosophy/zAH5PVD6">philosophy</a> for minimizing the possible <a href="/facts/Loss_function/xv5ozuhl">loss</a> for a <a href="/facts/Worst-case_scenario/tjofA08J">worst case (maximum loss) scenario</a>. When dealing with gains, it is referred to as "maximin" – to maximize the minimum gain. Originally formulated for several-player <a href="/facts/Zero-sum/ccouScgK">zero-sum</a> <a href="/facts/Game_theory/zkEIj1ya">game theory</a>, 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.

Minimax open-in-new

Minimax