By using a minimax search, all we have to do is program, in a game playing situation our agent to look at the whole search tree from the current state of the game, and select the minimax solution before making a move. Unluckily, only in very trivial games like the one above is it possible to calculate the minimax answer all the way from the end states in a game. So, for games of higher complexity, we are forced to estimate the minimax option for world states using an evaluation function. Of course, this is a heuristic function .
In a normal minimax search, we write down the whole search space and then propogate the scores from the goal states to the top of the tree so that we can choose the best move for a player. In a cut off search, however, we write down the entire search space up to a specific depth and then note down the evaluation function for each of the states at the bottom of the tree. We then propagate these values from the bottom to top the same way in exactly ,as minimax.
In advance, the depth is decided to ensure that the agent does not take too long to select a move.if it has longer, then we permit it to go deeper. If our agent has a given time restriction for each move, then it makes sense to enable it to continue searching until the time runs out. There are several ways to do the search in such a way that a game playing agent searches as much as possible in the time available. For an exercise, what possible ways can you find out of to perform this search? It is essential to bear in mind that the point of the search is not to find a node in the above graph but it is to determine which move the agent should make.