Relation to search A tree of such evaluations is usually part of a search algorithm, such as Monte Carlo tree search or a 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.
Relation to search A tree of such evaluations is usually part of a search algorithm, such as Monte Carlo tree search or a 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 po.sition (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 abs,olute rather than heuristic, if a win, loss or draw occurs at the node.