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moha_10 Group Title

quyz plz help

  • one year ago
  • one year ago

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  1. moha_10 Group Title
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    • one year ago
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  2. moha_10 Group Title
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    plz help

    • one year ago
  3. annas Group Title
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    moha Is it C language or java?

    • one year ago
  4. annas Group Title
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    http://cs.ucla.edu/~rosen/161/notes/alphabeta.html try this

    • one year ago
  5. moha_10 Group Title
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    ammmmmmmm may be java

    • one year ago
  6. moha_10 Group Title
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    anaas can u plz guide me to solve

    • one year ago
  7. annas Group Title
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    moha its an algorithm let me read it first then i can guide ok

    • one year ago
  8. moha_10 Group Title
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    okay thank u very much

    • one year ago
  9. annas Group Title
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    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.

    • one year ago
  10. annas Group Title
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    In computer science, a search algorithm is an algorithm for finding an item with specified properties among a collection of items

    • one year ago
  11. annas Group Title
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    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
  12. moha_10 Group Title
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    alright

    • one year ago
  13. annas Group Title
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    It is an adversarial search algorithm used commonly for machine playing of two-player games (Tic-tac-toe, 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
  14. moha_10 Group Title
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    okay

    • one year ago
  15. annas Group Title
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    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, depth-1, α, β, not(Player) )) if β ≤ α break (* Beta cut-off *) return α else for each child of node β := min(β, alphabeta(child, depth-1, α, β, not(Player) )) if β ≤ α break (* Alpha cut-off *) return β (* Initial call *) alphabeta(origin, depth, -infinity, +infinity, MaxPlayer)

    • one year ago
  16. annas Group Title
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    Beta is the minimum upper bound of possible solutions

    • one year ago
  17. annas Group Title
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    Alpha is the maximum lower bound of possible solutions

    • one year ago
  18. moha_10 Group Title
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    okay thoes just assumption right

    • one year ago
  19. moha_10 Group Title
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    ???

    • one year ago
  20. moha_10 Group Title
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    ur two last response i meant

    • one year ago
  21. annas Group Title
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    yes

    • one year ago
  22. annas Group Title
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    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
  23. moha_10 Group Title
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    alright

    • one year ago
  24. annas Group Title
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    @moha_10 http://cs.ucla.edu/~rosen/161/notes/alphabeta.html there are couple of examples that will help you

    • one year ago
  25. moha_10 Group Title
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    okay nice

    • one year ago
  26. annas Group Title
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    i think this will help you alot :)

    • one year ago
  27. moha_10 Group Title
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    i'll try it

    • one year ago
  28. annas Group Title
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    ok do try it. there is a saying "practice makes perfect " :)

    • one year ago
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