[Haskell-cafe] Code from "Why Functional Programming Matters"

[Andrew Wagner]

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?

Hughes seems to completely ignore this, and I wonder if it's because
it gets ugly to implement.