Completeness Is Not Optional — Why Game-Playing AI Finally Learned to Finish What It Starts
The algorithm did not lose because it was shallow Endgames are where polite uncertainty goes to die. Early in a game, a search algorithm can afford approximation. The tree is huge, the clock is rude, and the best it can do is lean on an evaluation function that says, with the usual machine confidence, “this line looks promising.” Fine. Nobody expects omniscience on move three. ...
