The algorithm maintains two values, alpha and beta, which represent.
Idea of Alpha-Beta Pruning Alpha-beta pruning avoids search that won’t change the minimax evaluation. Example: If MAX has a move with value 3, stop searching other moves known to be ≤3.
General Principle: Consider a node N. α = largest v in MAX ancestors of N. β = smallest v in MIN ancestors of N. If α ≥β, processing N cannot change eval. Alpha-beta pruning Traverse search tree in depth-first order At MAX node n, alpha(n) = max value found so far Alpha values start at -∞ and only increase At MIN node n, beta(n) = min value found so far Beta values start at +∞ and only decrease Beta cutoff: stop search below MAX node N (i.e., don’t examine more descendants) if alpha(N) >.
Alpha–beta pruning is a search algorithm that seeks to decrease the number of nodes that are evaluated by the minimax algorithm in its search tree. It is an adversarial search algorithm used commonly for machine playing of two-player games. It stops evaluating a move when at least one possibility has been found that proves the move to be worse than a previously examined move. Such Class: Search algorithm. Oct 22, Example: Rock, Paper, Scissors Two players, each simultaneously chooses Rock, Paper or Scissors.
Rock beats Scissors, Scissors beats Paper, Paper beats Rock. When Σ ui = 0, we call this a zero-sum game.
Wilson Peyton Young.
Otherwise, general-sum. Alpha-beta pruning avoids search that won’t change the minimax evaluation. Example: If MAX has a move with value 3, stop searching other moves known to be ≤3. General Principle: Consider a node N. α = largest v in MAX ancestors of N. β = smallest v in MIN ancestors of. Pruning What’s really needed is “smarter” more efficient search –Don’t expand “dead-end” nodes!
•Pruning–eliminating a branch of the search tree from consideration Alpha-beta pruning, applied to a minimax tree, returns the same “best” move, while pruning away unnecessary branches –Many fewer nodes might be expanded.