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anonymous
 4 years ago
quyz plz help
anonymous
 4 years ago
quyz plz help

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anonymous
 4 years ago
Best ResponseYou've already chosen the best response.0moha Is it C language or java?

anonymous
 4 years ago
Best ResponseYou've already chosen the best response.0ammmmmmmm may be java

anonymous
 4 years ago
Best ResponseYou've already chosen the best response.0anaas can u plz guide me to solve

anonymous
 4 years ago
Best ResponseYou've already chosen the best response.0moha its an algorithm let me read it first then i can guide ok

anonymous
 4 years ago
Best ResponseYou've already chosen the best response.0okay thank u very much

anonymous
 4 years ago
Best ResponseYou've already chosen the best response.0Alphabeta pruning is a search algorithm that seeks to decrease the number of nodes that are evaluated by the minimax algorithm in its search tree.

anonymous
 4 years ago
Best ResponseYou've already chosen the best response.0In computer science, a search algorithm is an algorithm for finding an item with specified properties among a collection of items

anonymous
 4 years ago
Best ResponseYou've already chosen the best response.0Minimax (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.

anonymous
 4 years ago
Best ResponseYou've already chosen the best response.0It 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.

anonymous
 4 years ago
Best ResponseYou've already chosen the best response.0Pseudocode: 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)

anonymous
 4 years ago
Best ResponseYou've already chosen the best response.0Beta is the minimum upper bound of possible solutions

anonymous
 4 years ago
Best ResponseYou've already chosen the best response.0Alpha is the maximum lower bound of possible solutions

anonymous
 4 years ago
Best ResponseYou've already chosen the best response.0okay thoes just assumption right

anonymous
 4 years ago
Best ResponseYou've already chosen the best response.0ur two last response i meant

anonymous
 4 years ago
Best ResponseYou've already chosen the best response.0Thus, when any new node is being considered as a possible path to the solution, it can only work if: alpha <= N <= beta

anonymous
 4 years ago
Best ResponseYou've already chosen the best response.0@moha_10 http://cs.ucla.edu/~rosen/161/notes/alphabeta.html there are couple of examples that will help you

anonymous
 4 years ago
Best ResponseYou've already chosen the best response.0i think this will help you alot :)

anonymous
 4 years ago
Best ResponseYou've already chosen the best response.0ok do try it. there is a saying "practice makes perfect " :)
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