It should determine as accurately as possible the likely outcome of the game from that position, however it doesn’t need to take account of a great deal of tactical information, as hopefully the tactics in the position are taken care of in the variations themselves. The evaluation function is usually applied to the terminal positions of the variations the computer considers. This method of growing a tree of possible moves, and then evaluating the resulting positions also works with games like Othello, and Checkers (Draughts), but is less useful with games where there are usually a large number of moves available like ‘Go’. The above is a gross simplification, but it captures the essence of the method, and allows us to understand a lot of what may go wrong. This is clearly impractical, a compromise is to grow the list of variations as large as possible, in the time permitted, and then use an evaluation function to try and decide the likely outcome from the final position of each variation. There is a simple way to play perfect chess, write down all the possible games of chess, note if the final position is won, drawn or lost, and then working backwards assume each player choses the best line, you will eventually end up with a list of all the best possible games of chess. Vital to understanding how to beat computers at chess is an understanding of how they play.
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