# The Fifteen Puzzle - The Algorithm

Before you read this, play with the above puzzle. You can move the blocks around yourself by clicking on one adjacent to the empty square. Click “shuffle” and the blocks will rearrange themselves using 25 randomly selected moves. Click “solve” from any configuration that isn’t already ordered 1-15 and you’ll see the blocks rearrange themselves.

If you hit shuffle more than 2 times, it’ll take some work to solve the puzzle, so you’ll see it solving for a while before it actually does anything. The version you see above is actually throttled so it doesn’t freeze your browser or use hundreds of MB of memory.

As always, you can see source for the above: github.com/jlfwong/fifteen-puzzle.

# Background

I’ve been going through Stanford’s free online Introduction to Artificial Intelligence with my housemates and have been enjoying them. My favourite thing from the first set of lectures was the example of heuristics being applied to the Fifteen Puzzle. So I wrote up a solver and made the interactive demo you see above. Start watching at Unit 2, Topic 31, Sliding Blocks Puzzle to see a great explanation of what I’m doing.

# Algorithm

The algorithm I used is pretty much exactly what’s described in the video. My solution in coffeescript is an optimized and throttled version of the following:

``````solve = (startGrid) ->
frontier = new PriorityQueue
frontier.enqueue(new SolverState(startGrid, []))

while not frontier.empty()
curState = frontier.dequeue()

if curState.solved
return curState.steps

candidateMoves = grid.validMoves()

for move in candidateMoves
nextGrid = grid.applyMove(move)
nextSteps = curState.steps.concat([move])
nextState = new SolverState(nextGrid, nextSteps)
frontier.enqueue(nextState)
``````

`SolverState` stores the current position of all the numbers in the grid and the list of steps to get there from the starting grid.

`PriorityQueue` is responsible for making sure we always explore the lowest cost state first. The cost of the state is the number of steps taken from the initial state plus the estimated number of steps remaining to get to the solution. This estimate (h2 from Unit 2, Topic 31) is admissible because each move can at best reduce that estimate by one.

`grid.validMoves` returns a list of all valid moves to make the on the grid. If the empty square is in the middle of the grid, this is all four directions. If it’s in a corner, there are only two valid directions.

# Optimizations

The algorithm described above will yield the optimal solution (fewest number of moves) for any valid fifteen-puzzle configuration. But it’s pretty slow and can be much improved.

## Tree Pruning

When looking at the search tree, there are some branches we can guarantee will not yield an optimal solution. The most obvious one here is to never go backwards. In the above algorithm, we can prevent going backwards like so:

``````lastStep = _.last(steps)
if lastStep?
candidates = _(candidates).filter (x) ->
not directionsAreOpposites x, lastStep
``````

I’m using underscore.js for its functional goodness, so that’s where `_.last` and `_.filter` come from.

## Las Vegas Randomization

While the algorithm always takes the lowest cost state, there will frequently be ties. Because of of the nature of how `validMoves` works, this means that the algorithm will make a disproportionate number of moves in the same direction. To fix this, we shuffle the list of valid moves before we start adding states to the priority queue. To do this, we change the `candidates = ...` line to be:

``````candidates = _.shuffle(grid.validMoves())
``````

A Las Vegas algorithm is always right and sometimes fast. This is in contrast to a Monte Carlo algorithm, which is always fast and sometimes right.

## Min-Heap Priority Queue

Once I applied the above to make the algorithm terminate in fewer iterations, it was time to optimize each iteration. By using Chrome Developer Tools, I was able to identify that the JavaScript runtime was spending most of its time in `ProrityQueue::enqueue`.

The first version I made of this used an `O(nlogn)` enqueue, `O(1)` dequeue method: I just appended and sorted after every enqueue and popped the element off the back of the array on dequeue.

Once I saw it was a bottleneck, I reimplemented the `PriorityQueue` as a min-heap to get `O(logn)` enqueues and dequeues, and this improved performance significantly.

You can see my implementation of a heap in coffeescript in solver.coffee.

## Leveraging Known Results

After I switched to a min-heap, the next bottleneck was calculating the heuristic estimate. The observation here that made this faster is that the heuristic value can be determined for a state based on the previous state and the move used to get there. This prevents the need to recalculate the estimate for all the grids generated.

You can see this update applied on the commit: Leverage known results for lowerSolutionBound.

That’s all for the algorithm. I’m planning to do two more posts about this project - one on using Chrome Developer Tools to optimize and how to not crash the browser, and another on the Jasmine test framework and literate programming with coffeescript. If you want to know when those come out, you should follow me on twitter @jlfwong.

If you liked reading this, you should subscribe by email, follow me on twitter, take a look at other blog posts by me, or maybe even come work with me at Figma!

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