Mini-Max Algorithm in Artificial Intelligence
Mini-max algorithm is a recursive or backtracking algorithm which is
used in decision-making and game theory. It provides an optimal move
for the player assuming that opponent is also playing optimally.
Mini-Max algorithm uses recursion to search through the game-tree.
Min-Max algorithm is mostly used for game playing in AI. Such as
Chess, Checkers, tic-tac-toe, go, and various tow-players game. This
Algorithm computes the minimax decision for the current state.
In this algorithm two players play the game, one is called MAX and
other is called MIN.
Both the players fight it as the opponent player gets the minimum
benefit while they get the maximum benefit.
Both Players of the game are opponent of each other, where MAX will
select the maximized value and MIN will select the minimized value.
The minimax algorithm performs a depth-first search algorithm for the
exploration of the complete game tree.
The minimax algorithm proceeds all the way down to the terminal node
of the tree, then backtrack the tree as the recursion.
Pseudo-code for MinMax Algorithm:
function minimax(node, depth, maximizingPlayer) is
if depth ==0 or node is a terminal node then
return static evaluation of node
if MaximizingPlayer then // for Maximizer Player
maxEva= -infinity
for each child of node do
eva= minimax(child, depth-1, false)
1
Mini-max algorithm is a recursive or backtracking algorithm which is
used in decision-making and game theory. It provides an optimal move
for the player assuming that opponent is also playing optimally.
Mini-Max algorithm uses recursion to search through the game-tree.
Min-Max algorithm is mostly used for game playing in AI. Such as
Chess, Checkers, tic-tac-toe, go, and various tow-players game. This
Algorithm computes the minimax decision for the current state.
In this algorithm two players play the game, one is called MAX and
other is called MIN.
Both the players fight it as the opponent player gets the minimum
benefit while they get the maximum benefit.
Both Players of the game are opponent of each other, where MAX will
select the maximized value and MIN will select the minimized value.
The minimax algorithm performs a depth-first search algorithm for the
exploration of the complete game tree.
The minimax algorithm proceeds all the way down to the terminal node
of the tree, then backtrack the tree as the recursion.
Pseudo-code for MinMax Algorithm:
function minimax(node, depth, maximizingPlayer) is
if depth ==0 or node is a terminal node then
return static evaluation of node
if MaximizingPlayer then // for Maximizer Player
maxEva= -infinity
for each child of node do
eva= minimax(child, depth-1, false)
1
