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annas Group TitleBest ResponseYou've already chosen the best response.1
moha Is it C language or java?
 2 years ago

annas Group TitleBest ResponseYou've already chosen the best response.1
http://cs.ucla.edu/~rosen/161/notes/alphabeta.html try this
 2 years ago

moha_10 Group TitleBest ResponseYou've already chosen the best response.0
ammmmmmmm may be java
 2 years ago

moha_10 Group TitleBest ResponseYou've already chosen the best response.0
anaas can u plz guide me to solve
 2 years ago

annas Group TitleBest ResponseYou've already chosen the best response.1
moha its an algorithm let me read it first then i can guide ok
 2 years ago

moha_10 Group TitleBest ResponseYou've already chosen the best response.0
okay thank u very much
 2 years ago

annas Group TitleBest 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.
 2 years ago

annas Group TitleBest 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
 2 years ago

annas Group TitleBest 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.
 2 years ago

annas Group TitleBest 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.
 2 years ago

annas Group TitleBest 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)
 2 years ago

annas Group TitleBest ResponseYou've already chosen the best response.1
Beta is the minimum upper bound of possible solutions
 2 years ago

annas Group TitleBest ResponseYou've already chosen the best response.1
Alpha is the maximum lower bound of possible solutions
 2 years ago

moha_10 Group TitleBest ResponseYou've already chosen the best response.0
okay thoes just assumption right
 2 years ago

moha_10 Group TitleBest ResponseYou've already chosen the best response.0
ur two last response i meant
 2 years ago

annas Group TitleBest 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
 2 years ago

annas Group TitleBest 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
 2 years ago

annas Group TitleBest ResponseYou've already chosen the best response.1
i think this will help you alot :)
 2 years ago

moha_10 Group TitleBest ResponseYou've already chosen the best response.0
i'll try it
 2 years ago

annas Group TitleBest ResponseYou've already chosen the best response.1
ok do try it. there is a saying "practice makes perfect " :)
 2 years ago
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