No Title When I finaly take it upon myself to write, this is what happens!!!

Water Jug Rewrite With Haskell Part I


Water jug problem is a famous problem commonly found in AI texts. There are few different version of it like these:


I also had to do the same problem in my own college days. At the time I was learning python and thought it might be an interesting problem to solve with python. Looking at the code now I feel embarrassed. Am not even sure how I got it working. Rewriting it in python seems rather uninteresting now. Rather using a static functional language like Haskell seamed to make it more interesting. Obviously once you look at the code you will realize currently I don’t know much of Haskell either.

Rather than trying to solve the problem in one shot, I decided to limit the initial version some what.

Updated Problem

There can be some Water Jars, with any given capacity. The challenge will be to start from that to reach the given final state which will be get some specified quantity of water in these Jars. These Jugs have no measurements. But you know the full capacity of the Jugs.

As a starting point max number of Jars will be limited to two.


One of the most interesting things about writing in a language like Haskell is how Types and data become powerful design tools. So to start with let us define Jug and State which is the combination of Jugs.

-- WaterJug.hs

--  Jug capacity, holding
data Jug = Jug Int Int deriving (Show, Eq, Ord)

-- State left right
data State = State Jug Jug deriving (Show, Eq, Ord)

-- Problem initial state, destination state
data Problem = Problem State State deriving (Show, Eq, Ord)

-- StateMap, type alias
type StateMap = M.Map State [State]

emptyJug :: Int -> Jug
emptyJug c = Jug c 0

newProblem :: Int -> Int -> Int -> Int -> Problem
newProblem rc lc r l = Problem (State (emptyJug rc) (emptyJug lc)) (State (Jug rc r) (Jug lc l))

The deriving ensures that we can have reasonable string representation for these objects, they can be equated to each other, ordered etc.

All these types we created using data are tuples. They can only contain two arguments. They are also positional.

emptyJug function can be used to create an empty Jug of any given capacity.

newProblem function can be used to define a problem which include jug capacities and the final state we want to achieve.

Possible Operations on Given Jugs

Given two Jugs you can do 6 operations. Depending on what state each Jugs are in only some of these operations will be valid at any given state.

The operations are

forRightFull :: State -> Maybe State
forRightFull (State (Jug rc rh) lj)
  | rc <= rh = Nothing
  | otherwise = Just $ State (Jug rc rc) lj

forRightToLeft :: State -> Maybe State
forRightToLeft (State (Jug rc rh) (Jug lc lh))
  | rh == 0 || lc <= lh = Nothing
  | otherwise = Just $ State (Jug rc (rh -liquidToTransfer)) (Jug lc (lh + liquidToTransfer))
      liquidToTransfer = if maxCanPour >= rh then rh else maxCanPour
      maxCanPour = lc - lh
forRightToEmpty :: State -> Maybe State
forRightToEmpty (State (Jug rc rh) lj)
  | rh == 0 = Nothing
  | otherwise = Just $ State (empty rc) lj

interchange :: (State -> Maybe State) -> State -> Maybe State
interchange f (State rj lj) =
  case f (State lj rj) of
    Nothing -> Nothing
    Just (State rj' lj') -> Just (State lj' rj')
forLeftFull :: State -> Maybe State
forLeftFull = interchange forRightFull

forLeftToRight :: State -> Maybe State
forLeftToRight = interchange forRightToLeft

forLeftToEmpty :: State -> Maybe State
forLeftToEmpty = interchange forRightToEmpty

Few interesting things to note here. All the action functions have the same signature. interchange takes a function as first parameter and we are partially applying it giving us the exact type signature we required. There common type allows us to put all these functions in a list. An important feature we will be using next. We use option(Maybe) type which allows us denote irrelevant actions on given state.

Compute Next States

Given any State, you can apply all these functions to get all the possible next State we can take the Jars to. I ended up doing that like so:

getNextState :: State -> [State]
getNextState s = catMaybes $ map (\f -> f s) toNextStates
    toNextStates = [forRightToLeft, forRightToEmpty, forLeftFull,
                    forLeftToRight, forLeftToEmpty, forRightFull]

toNextStates is a list of the functions that were defined in the previous section. The only way all these functions can be put in a single list is if they have the same signature.

catMaybes filters out all the Nothings and takes out the Just values. Thus leaving us with actual/relevant changes.

Finding relevent states

Now given a starting state and final state, we try to find all the possible states between them. The allStates function is a kind of wrapper for the inner allStates' function. allStates calls the inner functions with a correct set of initial arguments, which implements most of the logic.

allStates :: Problem -> StateMap
allStates (Problem i f) =
    allStates' :: State -> S.Set State -> StateMap -> StateMap
    allStates' current queue m
      | S.null queue = m
      | otherwise = allStates' next queue'' m'
        ns = getNextState current
        -- insert current state and its possible transitions to the StateMap
        m' = M.insert current ns m
        queue' = S.delete current queue
        -- filter out all the states that have already been visited.
        new_q = S.fromList $ filter (\s -> M.notMember s m) ns
        -- if final state is in one of these, we dont care for other transitions
        -- from that point
        queue'' = if S.member f new_q
          then queue'
          else S.union queue' new_q
        next = head $ S.toList queue''
    allStates' i (S.fromList [i]) M.empty

You can think of the where clauses as being executed top to bottom. In reality though they are all lazy and could be executed as and when the need arises. So order has no meaning.

Is there a possible path from initial to final state?

Once we have the StateMap we should be able to find if there is a possible path from initial to final state. For that we flatten out the values of StateMap and check if the final state is one of State.

isPathPossible :: Problem -> StateMap -> Bool
isPathPossible (Problem _ f) m = S.member f $ S.fromList $ concat $ M.elems m

Find Paths from Initial to Final State

Once we know there is a path, we walk the StateMap depth first to find possible paths to the final state. We look at this like a tree with initial state as the root node. When we find a path that reaches the final state we add that to possible paths and continue with our search. One of the optimizations we do is, if we find cycles, ie same state being repeated we end our search on that branch. Similarly if a state is not in StateMap once again we end our search on that branch. In all other cases we fold over next states and call findPaths' recursively. findPaths and findPaths' are similar to allStates and allStates' in that findPaths is mostly a limited wrapper over the inner function which implements most of the logic.

findPaths :: Problem -> StateMap -> [[State]]
findPaths (Problem i f) m =
  findPaths' [i] []
    findPaths' :: [State] -> [[State]] -> [[State]]
    findPaths' (x:xs) ps
      -- we have found a cycle, return the routes we have found
      -- and end the search on this branch
      | x `elem` xs = ps
      -- found a full path, add that to routes and end branch
      | x == f  = (reverse $ x:xs):ps
      | otherwise = case M.lookup x m of
                      Nothing -> ps
                      Just ls -> foldl (\ps' l-> findPaths' (l:x:xs) ps') ps ls

Shortest of the Paths

Now that we have a bunch of candidates for paths to final state, its time to find the shortest one. For that we just compare the length of each of the paths like so:

shortestPath :: [[State]] -> [State]
shortestPath ps = foldl (\a x -> if (length a) > (length x)
                            then x else a) (head ps) (tail ps)

Putting it all together

Now we have all the pieces to solve a given Problem.

solve :: Problem -> Maybe [State]
solve p = if isPathPossible p ss
          then Just $ shortestPath $ findPaths p ss
          else Nothing
    ss = allStates p

The returned type is a bit interesting, it says given a Problem maybe we have a solution to get from initial to final state. :)


To conclude for now let me present a simple REPL session.

> :l WaterJug.hs
> solve $ newProblem 4 3 2 2
> solve $ newProblem 5 3 4 0
Just [State (Jug 5 0) (Jug 3 0),State (Jug 5 5) (Jug 3 0),State (Jug 5 2) (Jug 3 3),State (Jug 5 2) (Jug 3 0),State (Jug 5 0) (Jug 3 2),State (Jug 5 5) (Jug 3 2),State (Jug 5 4) (Jug 3 3),State (Jug 5 4) (Jug 3 0)]
> solve $ newProblem 4 3 2 0
Just [State (Jug 4 0) (Jug 3 0),State (Jug 4 0) (Jug 3 3),State (Jug 4 3) (Jug 3 0),State (Jug 4 3) (Jug 3 3),State (Jug 4 4) (Jug 3 2),State (Jug 4 0) (Jug 3 2),State (Jug 4 2) (Jug 3 0)]

Next time we will try to solve this for n jugs.