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annasBest ResponseYou've already chosen the best response.1
moha Is it C language or java?
 one year ago

annasBest ResponseYou've already chosen the best response.1
http://cs.ucla.edu/~rosen/161/notes/alphabeta.html try this
 one year ago

moha_10Best ResponseYou've already chosen the best response.0
anaas can u plz guide me to solve
 one year ago

annasBest ResponseYou've already chosen the best response.1
moha its an algorithm let me read it first then i can guide ok
 one year ago

moha_10Best ResponseYou've already chosen the best response.0
okay thank u very much
 one year ago

annasBest ResponseYou've already chosen the best response.1
Alphabeta pruning is a search algorithm that seeks to decrease the number of nodes that are evaluated by the minimax algorithm in its search tree.
 one year ago

annasBest ResponseYou've already chosen the best response.1
In computer science, a search algorithm is an algorithm for finding an item with specified properties among a collection of items
 one year ago

annasBest ResponseYou've already chosen the best response.1
Minimax (sometimes minmax) is a decision rule used in decision theory, game theory, statistics and philosophy for minimizing the possible loss for a worst case (maximum loss) scenario.
 one year ago

annasBest ResponseYou've already chosen the best response.1
It is an adversarial search algorithm used commonly for machine playing of twoplayer games (Tictactoe, Chess, Go, etc.). It stops completely evaluating a move when at least one possibility has been found that proves the move to be worse than a previously examined move. Such moves need not be evaluated further. When applied to a standard minimax tree, it returns the same move as minimax would, but prunes away branches that cannot possibly influence the final decision.
 one year ago

annasBest ResponseYou've already chosen the best response.1
Pseudocode: function alphabeta(node, depth, α, β, Player) if depth = 0 or node is a terminal node return the heuristic value of node if Player = MaxPlayer for each child of node α := max(α, alphabeta(child, depth1, α, β, not(Player) )) if β ≤ α break (* Beta cutoff *) return α else for each child of node β := min(β, alphabeta(child, depth1, α, β, not(Player) )) if β ≤ α break (* Alpha cutoff *) return β (* Initial call *) alphabeta(origin, depth, infinity, +infinity, MaxPlayer)
 one year ago

annasBest ResponseYou've already chosen the best response.1
Beta is the minimum upper bound of possible solutions
 one year ago

annasBest ResponseYou've already chosen the best response.1
Alpha is the maximum lower bound of possible solutions
 one year ago

moha_10Best ResponseYou've already chosen the best response.0
okay thoes just assumption right
 one year ago

moha_10Best ResponseYou've already chosen the best response.0
ur two last response i meant
 one year ago

annasBest ResponseYou've already chosen the best response.1
Thus, when any new node is being considered as a possible path to the solution, it can only work if: alpha <= N <= beta
 one year ago

annasBest ResponseYou've already chosen the best response.1
@moha_10 http://cs.ucla.edu/~rosen/161/notes/alphabeta.html there are couple of examples that will help you
 one year ago

annasBest ResponseYou've already chosen the best response.1
i think this will help you alot :)
 one year ago

annasBest ResponseYou've already chosen the best response.1
ok do try it. there is a saying "practice makes perfect " :)
 one year ago
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