Editing Evaluation function (section)

==Relation to search==
A tree of such evaluations is usually part of a search algorithm, such as [[Monte Carlo tree search]] or a  [[Minimax#Minimax algorithm with alternate moves|minimax algorithm]] like [[alpha–beta search]]. The value is presumed to represent the relative probability of winning if the game tree were expanded from that node to the end of the game. The function looks only at the current position (i.e. what spaces the pieces are on and their relationship to each other) and does not take into account the history of the position or explore possible moves forward of the node (therefore static). This implies that for dynamic positions where tactical threats exist, the evaluation function will not be an accurate assessment of the position.  These positions are termed non-''quiescent''; they require at least a limited kind of search extension called [[quiescence search]] to resolve threats before evaluation.  Some values returned by evaluation functions are absolute rather than heuristic, if a win, loss or draw occurs at the node.

There is an intricate relationship between search and knowledge in the evaluation function. Deeper search favors less near-term tactical factors and more subtle long-horizon positional motifs in the evaluation.  There is also a trade-off between efficacy of encoded knowledge and computational complexity: computing detailed knowledge may take so much time that performance decreases, so approximations to exact knowledge are often better.  Because the evaluation function depends on the nominal depth of search as well as the extensions and reductions employed in the search, there is no generic or stand-alone formulation for an evaluation function.  An evaluation function which works well in one application will usually need to be substantially re-tuned or re-trained to work effectively in another application.