The horizon effect, also known as the horizon problem, is a problem in artificial intelligence whereby, in many games, the number of possible states or positions is immense and computers can only feasibly search a small portion of them, typically a few plies down the game tree.
Thus, for a computer searching only a fixed number of plies, there is a possibility that it will make a detrimental move, but the effect is not visible because the computer does not search to the depth at which its evaluation function reveals the true evaluation of the line (i.e., beyond its "horizon").
When evaluating a large game tree using techniques such as minimax with alpha-beta pruning, search depth is limited for feasibility reasons.
In 1973 Hans Berliner named this phenomenon, which he and other researchers had observed, the "Horizon Effect.
This gives the search algorithm ability to look beyond its horizon for a certain class of moves of major importance to the game state, such as captures in chess.