Sunday, November 1, 2009

Knight's Tour

I am taking a Graph Theory class at my school, and last class my teacher handed out a problem to the class. He isn't going to collect the problem, and we are not even expected to solve the problem, but he thought that some of the class would appreciate it.

The problem is fairly simple. On a chessboard, there are 64 squares arranged in an 8x8 grid. A knight is capable of moving in an "L" shape, 2 squares in one direction and 1 square in an orthogonal direction in a single move. A knight starts out at the top left corner of the chess board. It has been shown that it is possible for the knight to move to each and every square on the chessboard exactly one time and end up in the top left corner again. What is the sequence of moves that the knight has to go through in order to complete this tour of the chessboard?

Before I begin, I want to say that I have not solved this problem yet. This post is not about a solution to the problem. It is about my thought processes and my ability to learn lessons from my failed attempt at solving this problem.

It is extraordinarily simple to reduce this into a graph problem; in fact, my professor did it for us. Each square on the chessboard represents a vertex, and there are edges going from each square to each of the 8 other squares that a knight can jump to from that square. The question is simply asking for a Hamiltonian Cycle starting from the top-left vertex. Now, it is well-known that the Hamiltonian Path problem is NP-Complete. That means that there is no quick algorithm for solving the problem (More formally, all known algorithms for solving this problem involve a branching factor that is proportional to the data size - think of it as having complexity of C^N rather than N^C.) Therefore, there is no quick straightforward algorithm to solve this problem (in the generic case).

I'll admit it - I'm not very good at math. Perhaps I have been tainted by computers, but it takes me a long time to do a problem like this. I have been jaded with the idea of solving problems by hand when I have a giant machine next to me that can solve it for me. All I have to do is tell the machine the rules of the problem, and away it goes! It sounds so much easier than actually having to make deductions about a problem.

The general problem itself is NP-Complete, so I am assuming that there are some additional rules that can be added because of the specific layout of this graph. However, my attempt at solving this problem immediately disregarded these additional rules. Instead of making intelligent deductions, I threw a standard algorithm and the basic rules at the computer and told it to go, in much the way that I have described above.

The actual algorithm I used is one that is a simple case of an Artificial Intelligence search algorithm. The idea is to make all the possible moves (in a structured order) and hopefully one of the moves is the "winning" move. Given a board, there is a set of moves that the knight can make, which result in a set of "neighboring" boards. Then, from each of these boards, the knight can move again, and again, so on and so forth. It is solved in much the same way that a constraint satisfaction problem is solved - follow possible path the knight can take all the way until it can't move anymore, and if that's not the winning move, back up a little and try a similar path with only the ending different. If that doesn't work, back up again, etc. This is a stack-based approach, which leads to a depth-first traversal of the problem.

I have had to implement this algorithm many times. Once was in Artificial Intelligence class, once was on my own (I was making a SuDoKu solver because I'm bad at math, and had to get a computer to solve the problem for me), and once in Computer Science 4 class. So needless to say, in an hour or two the algorithm was coded up in Haskell (my current favorite language) and here is my code for you to peruse:
`import qualified Data.Setdata Board = Board [[Bool]] Int Int deriving (Show, Eq, Ord)set2DIndex :: [[a]] -> Int -> Int -> a -> [[a]]set2DIndex l x y v =   [l !! c | c <- [0..x-1]] ++   [[(l !! x) !! c | c <- [0..y-1]] ++      [v] ++       [(l !! x) !! c | c <- [y+1..7]]] ++   [l !! c | c <- [x+1..7]]setVisited :: [Board] -> [Board]setVisited boards =   [Board (set2DIndex visited posx posy True) posx posy | Board visited posx posy <- boards]getSuccessors :: Board -> [Board]getSuccessors (Board visited posx posy) =   setVisited \$   filter (\(Board visited x y) -> not ((visited !! x) !! y)) \$   filter (\(Board visited x y) -> x >= 0 && x < 8 && y >= 0 && y < 8) \$   [Board visited (x + posx) (y + posy) | x <- [-1, 1], y <- [-2, 2]] ++   [Board visited (x + posx) (y + posy) | y <- [-1, 1], x <- [-2, 2]]isWin :: Board -> BoolisWin (Board visited _ _) = foldl1 (&&) \$ foldl1 (++) visited-- Receives a queue/stack of board histories and a set of processed boards, returns the sequence of boards that get the solutionfindTour :: [[Board]] -> Data.Set.Set Board -> [Board]findTour queue processed   | isWin board = last queue   | Data.Set.member board processed = findTour (init queue) processed   | otherwise = findTour ((init queue) ++ (map ((++) (last queue)) [[a] | a <- getSuccessors board])) (Data.Set.insert board processed)   where board = last \$ last queuemain = putStrLn \$ show \$ findTour [[Board [[False | x <- [1..8]] | y <- [1..8]] 0 0]] Data.Set.empty`
Sorry about the lack of comments - I was never really intending this to be shown to a large audience. Hopefully the code is not too unreadable.

So I hit the big proverbial "RUN" button, and watched the Activity Monitor as GHC started eating all my CPU and memory. Then, the CPU graph fell off and because stable at around 10%, with around 70% of that being system time. In addition, the system froze up, last.fm stopped playing music, and the mouse would respond only sporadically. Clearly, the system was swapping heavily.

While I'm watching my computer slowly toast itself alive, I took a step back and thought about the problem I was trying to solve. I thought about the other problems that I had solved with this same standard algorithm. SuDoKu ran quickly (About 0.1 seconds if I remember correctly) because the search tree could be pruned so easily. A naive interpretation of the data structure would be that there are 9x9 cells, and each one could have any of the number 1..9 in it, so that makes for 9^81 possible boards. However, most of those are not valid (e.g. any subtree of a board starting with 9,9 is invalid), so entire giant subtrees are taken out of the search path. This makes for a well-pruned search tree that can be traversed fairly quickly.

The knight's tour problem, however, does not lend itself very well to pruning. Sure, the knight cannot move to a spot that it has already moved to, but the probability of that happening is fairly low for most of the calculation. In fact, given the state representation that I chose (a 8x8 grid of booleans representing whether the knight has been at that location or not), not a single state is invalid. Every combination of those boolean values represents a valid board, unlike now a SuDoKu board has many invalid states. My algorithm eventually is going to have to check each of the 2^64 states that the board could be in, not to mention that two states can be different even if the boards are the same but the knight is in a different position. In fact, because there is no search pruning, my approach is no better than simply running through each of these possibilities one by one. Given that there are 10^19 possibilities, clearly this is not going to work.

As soon as I realized this, the Control-C came crashing down. Granted, the system took about 5 minutes to respond to the keypresses, but after everything that had been shunted to the disk was read back into memory, everything appears to be in working order now. Then, I ate some spaghetti.

During the meal, I thought to myself that this phenomena had not been the first time that this had occurred to me. There have been at least 2 or 3 problems (mostly Project Euler problems, as these are designed to exploit this problem) where I have immediately launched into coding up what I thought was a solution to the problem without realizing the complexity of the solution. In particular, even a linear-time algorithm can be slow if N is larger than about 10^9.

The underlying problem is that I do not have a very intuitive knowledge of how big 'big' is. I see a computationally-expensive problem, and think "Oh, that's more than I can do, but a computer can also do more than I can do, so surely a computer can do that task!" It takes a special kind of programmer to not just launch into a problem, but to take a step back and to realize the complexity of the problem, as well as other implicit problems such as data overrun problems and the like. I heard a story that Donald Knuth, in his day, would want to write a program, but instead of sitting down at a terminal, he would think about the problem for weeks in a row. Finally, when he was ready, he would sit down at his desk with paper and pencil, and write out his program from start to finish, in one swath. This means that before he wrote code, he thought about all the details of the algorithm, the complexity, the formulation, everything. Now, clearly this isn't incremental improvements (which are almost always a good thing), but I want to take a lesson from Knuth. I want to know what I'm writing before I write it, not as or after I write it.

Note: My Project Euler profile can be found here.