On Mon, 3 Sep 2007, Andrew Wagner wrote:
I've been reading the classic "Why functional programming matters" paper [1] lately, particularly looking at the alpha beta stuff. I've ported all his code to haskell, but I have a question.
His algorithm takes a board position, creates a gametree out of it, maps a static evaluation function over the gametree, then uses alpha beta to find the real evaluation from a Tree Int. But of course, in an actual application, what you actually want is the best MOVE from the current position (or, even more ideally, the so-called "principal variation", which is the best series of moves from the current position). Is there a good way to collect this, without mapping some sort of function over the tree that puts a list of moves on every node too?
I think he respects that, because every node in the game tree represents one possible state of the game. The "static" evaluation function pays attention to all information of each possible "current position".