Open study

is now brainly

With Brainly you can:

  • Get homework help from millions of students and moderators
  • Learn how to solve problems with step-by-step explanations
  • Share your knowledge and earn points by helping other students
  • Learn anywhere, anytime with the Brainly app!

A community for students.

quyz plz help

Computer Science
See more answers at brainly.com
At vero eos et accusamus et iusto odio dignissimos ducimus qui blanditiis praesentium voluptatum deleniti atque corrupti quos dolores et quas molestias excepturi sint occaecati cupiditate non provident, similique sunt in culpa qui officia deserunt mollitia animi, id est laborum et dolorum fuga. Et harum quidem rerum facilis est et expedita distinctio. Nam libero tempore, cum soluta nobis est eligendi optio cumque nihil impedit quo minus id quod maxime placeat facere possimus, omnis voluptas assumenda est, omnis dolor repellendus. Itaque earum rerum hic tenetur a sapiente delectus, ut aut reiciendis voluptatibus maiores alias consequatur aut perferendis doloribus asperiores repellat.

Get this expert

answer on brainly

SEE EXPERT ANSWER

Get your free account and access expert answers to this and thousands of other questions

1 Attachment
plz help
moha Is it C language or java?

Not the answer you are looking for?

Search for more explanations.

Ask your own question

Other answers:

http://cs.ucla.edu/~rosen/161/notes/alphabeta.html try this
ammmmmmmm may be java
anaas can u plz guide me to solve
moha its an algorithm let me read it first then i can guide ok
okay thank u very much
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.
In computer science, a search algorithm is an algorithm for finding an item with specified properties among a collection of items
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.
alright
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.
okay
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)
Beta is the minimum upper bound of possible solutions
Alpha is the maximum lower bound of possible solutions
okay thoes just assumption right
???
ur two last response i meant
yes
Thus, when any new node is being considered as a possible path to the solution, it can only work if: alpha <= N <= beta
alright
@moha_10 http://cs.ucla.edu/~rosen/161/notes/alphabeta.html there are couple of examples that will help you
okay nice
i think this will help you alot :)
i'll try it
ok do try it. there is a saying "practice makes perfect " :)

Not the answer you are looking for?

Search for more explanations.

Ask your own question