
Ronald Guida wrote:
Hi,
I'm trying to solve the N-queens problem, but with a catch: I want to generate solutions in a random order.
I know how to solve the N-queens problem; my solver (below) generates all possible solutions. What I am trying to do is generate solutions in a random order by somehow randomizing the order in which "nextRow" considers the unused columns. I tried adding a random number generator to the solution state; the problem with this approach is that whenever the solver backtracks, the state of the random number generator backtracks along with it. In effect, I am selecting a random, but fixed, permutation for each row, and then I am applying that same set of permutations along all computational paths. Whenever I consider row R, regardless of which path I have taken, I am applying row R's permutation to the unused columns.
This is not the behavior I want. I want each computational path to use a new, different permutation for each row. On the other hand I also want to be able to take the first few solutions without waiting for all possible solutions to be generated. How might I go about doing this?
[...] data (RandomGen g) => SolutionState g = SolutionState { solnBoard :: Board , solnUnusedColumns :: [Int] , solnRandomGen :: g }
nextRow :: (RandomGen g) => Int -> Int -> StateT (SolutionState g) [] ()
It's a matter of choosing the right monad stack. In particular, putting the random number generator into the solution state pretty much forces the undesired behavior. Random numbers are best put in a separate monad (transformer), for reasons of abstraction which are outlined here: http://lukepalmer.wordpress.com/2009/01/17/use-monadrandom/ http://apfelmus.nfshost.com/articles/random-permutations.html Also, it's not really necessary to use the state monad to store the solution, using a plain old parameter works just fine, as the following code illustrates: import Control.Monad.Random -- from the MonadRandom package -- generate a random permutation randomPerm :: MonadRandom r => [a] -> r [a] randomPerm xs = go (length xs) xs where go 0 [] = return [] go n xs = do k <- getRandomR (0,n-1) let (x,xs') = select k xs liftM (x:) $ go (n-1) xs' select 0 (x:xs) = (x,xs) select k (x:xs) = let (y,ys) = select (k-1) xs in (y,x:ys) -- 8 queens type Pos = (Int,Int) attacks (x1,y1) (x2,y2) = x1 == x2 || y1 == y2 || x1 - x2 == y1 - y2 || x2 - x1 == y1 - y2 type Solution = [Pos] solve :: Rand StdGen [Solution] solve = solve' 8 [] where solve' 0 qs = return [qs] solve' row qs = liftM concat . mapM putQueen =<< randomPerm [1..8] where putQueen col | any (q `attacks`) qs = return [] | otherwise = solve' (row-1) (q:qs) where q = (row,col) test seed = evalRand solve $ mkStdGen seed Regards, Heinrich Apfelmus -- http://apfelmus.nfshost.com