It mitigates the effect of the horizon problem faced by AI engines for various games like chess and Go.
As the main motive of quiescence search is to get a stable value out of a static evaluation function, it may also make sense to detect wide fluctuations in values returned by a simple heuristic evaluator over several ply, i.e. a history criterion.
In highly "unstable" games like Go and reversi, a rather large proportion of computer time may be spent on quiescence searching.
The horizon effect is a problem in artificial intelligence which can occur when all moves from a given node in a game tree are searched to a fixed depth.
This can result in the peculiar ploy of a program making delaying moves that degrade the position until it pushes a threat beyond the search depth or "horizon".