Contents
Which is the best description of the minimax rule?
Minimax (sometimes MinMax, MM or saddle point) is a decision rule used in artificial intelligence, decision theory, game theory, statistics, and philosophy for mini mizing the possible loss for a worst case ( max imum loss) scenario. When dealing with gains, it is referred to as “maximin”—to maximize the minimum gain.
How is minimax used in turn based games?
It is widely used in two player turn-based games such as Tic-Tac-Toe, Backgammon, Mancala, Chess, etc. In Minimax the two players are called maximizer and minimizer. The maximizer tries to get the highest score possible while the minimizer tries to do the opposite and get the lowest score possible.
What kind of algorithm is the minimax algorithm?
A minimax algorithm is a recursive algorithm for choosing the next move in an n-player game, usually a two-player game. A value is associated with each position or state of the game.
What does the minimax function do in Excel?
The minimax function returns a heuristic value for leaf nodes (terminal nodes and nodes at the maximum search depth). Non-leaf nodes inherit their value from a descendant leaf node. The heuristic value is a score measuring the favorability of the node for the maximizing player.
Minimax (sometimes MinMax, MM or saddle point) is a decision rule used in artificial intelligence, decision theory, 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.
How is minimax used in combinatorial game theory?
Combinatorial game theory. In combinatorial game theory, there is a minimax algorithm for game solutions. A simple version of the minimax algorithm, stated below, deals with games such as tic-tac-toe, where each player can win, lose, or draw. If player A can win in one move, their best move is that winning move.
Is the minimax solution the same as the Nash equilibrium?
In two-player zero-sum games, the minimax solution is the same as the Nash equilibrium. In the context of zero-sum games, the minimax theorem is equivalent to: (a) Given player 2’s strategy, the best payoff possible for player 1 is V, and. (b) Given player 1’s strategy, the best payoff possible for player 2 is −V.
Why is minimax important in non zero sum games?
“Maximin” is a term commonly used for non-zero-sum games to describe the strategy which maximizes one’s own minimum payoff. In non-zero-sum games, this is not generally the same as minimizing the opponent’s maximum gain, nor the same as the Nash equilibrium strategy. The minimax values are very important in the theory of repeated games.
How is the maximin rule used in decision making?
The maximin rule involves selecting the alternative that maximises the minimum pay-off achievable. The investor would look at the worst possible outcome at each supply level, then selects the highest one of these. The decision maker therefore chooses the outcome which is guaranteed to minimise his losses.
Which is the best strategy for minimax regret?
This ensures that the worst possible scenario still results in a gain of at least $80. The minimax regret strategy is the one that minimises the maximum regret. It is useful for a risk-neutral decision maker. Essentially, this is the technique for a ‘sore loser’ who does not wish to make the wrong decision.