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Cutoff Search :
To require a mini and max search in a game on stage situation is, in which all we have is just do that programme our agent to look at the intact that search tree from the current state of the game, and choose the mini and max solution just before making a move. Regrettably only if in mostly trivial games like the one above is it mostly possible and again possible to calculate the mini-max answer all the way from the end states in a game. Then we considered that, to games of higher complication we are forced to estimate the mini--max choice for world states need such an evaluation function. This is, palpably a heuristic function like as those we discussed in the lecture on search.
During just in a normal mini--max search, we also write down the whole search space and then propogate the scores from the goal states to the top of the tree that the best move for a player, we can choose for this. In a cutoff search, forever, our capacity to write down the complete search space up to a justify depth, after then write down the evaluation function at the bottom of the tree for each of the states. So that we will propagate these values from the bottom to the top in right the matching way as minimax.
It is fast because it has got separate program and data memory(highly pipelined architecture)
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