Solving the Boggle Game - Recursion, Prefix Tree, and Dynamic Programming

I spent this past weekend designing the game of Boggle. Although it looks like a simple game at a high level, implementing it in a programming language was a great experience. I had to use recursion, sorting, searching, prefix trees (also knows as trie's), and dynamic programming as I was improving the run time of the program. So here is a summary of the work I did in trying to write a very optimal solution to the Boggle game:

*Image courtesy: http://stackoverflow.com/questions/746082/how-to-find-list-of-possible-words-from-a-letter-matrix-boggle-solver

The game of Boggle is played on a N x N board (usually made of cubes that has letters engraved on it). Given a dictionary, you will have to construct words on the board following these rules:

i) The letters in the word must be "adjacent" to each other
ii) Two letters on the board are "adjacent" if they are located on the board next to each other horizontally, vertically, or diagonally.
iii) A word must be 3 or more letters long to be valid

The more words you can construct, the more points you get. Longer words get more points.

In computerse: given an N x N board, and a dictionary containing hundreds of thousands of words, construct those words from the board that are found in the dictionary.

There are a number of approaches that one can take to solve this problem. I designed three different algorithms to solve it. Here are they:

First solution - Recursion + Binary Search:

In this approach, we recurse (using backtracking) through the board and generate all possible words. For each word that are three or more letters long we check to see if it's in the dictionary. If it is, we have a match!

Here is the algorithm steps:

1. Read in the dictionary in to a container in memory.
2. Sort the dictionary.
3. Using backtracking, search the board for words.
4. If a word is found and it contains 3 or more letters, do a binary search on the dictionary to see if the word is there.
5. If searching was successful in the previous step, print the letter out.
6. Continue step 3-5 as long as there are more words to construct on the board.

Complexity of this approach:

In this solution, we do a good job on the dictionary search. Using binary search, we are quickly finding out whether a word is in dictionary or not. But the real bottleneck is in searching the board for words. For an N x N board the search space is O((N*N)!). I am not exactly sure about this number, but you can find some discussions of it here: http://www.danvk.org/wp/2007-08-02/how-many-boggle-boards-are-there/.
(N*N)! is a HUGE number even for N = 5. So this approach is is impractical and out of question for any useful implementation.


Second Solution - Pruned Recursion + Prefix Tree (also known as a Trie):

From the previous approach, our major concern was the enormous search space on the board. Fortunately, using a a prefix tree or trie data structure we could significantly cut down on this search space. The reasoning behind this improvement is that, if a word "abc" does not occur as a prefix to any word in the dictionary there is no reason to keep searching for words after we encounter "abc" on the board. This actually cut down the run time a lot.

Here is the algorithm steps:

1. Read a word from the dictionary file.
2. Insert it into a prefix tree data structure in memory.
3. Repeat steps 1-2 until all words in the dictionary have been inserted into the prefix tree.
4. Using backtracking, search the board for words.
5. If a word is found and it contains 3 or more letters, search the prefix tree for the word.
6. If searching was *not* successful in the previous step, return from this branch of the backtracking stage. (There is no point to continue searching in this branch, nothing in the dictionary as the prefix tree says).
7. If searching was successful in step 5, continue searching by constructing more words along this branch of backtracking and stop when the leaf node has been reached in the prefix tree. (at that point there is nothing more to search).
8. Repeat steps 4-7 as long as there are more words to search in the backtracking.

Complexity of this approach:

This approach significantly improves on the first one. Building a prefix tree our of the dictionary words is O(W * L), where W is the number of words in the dictionary and L is the maximum length of a word in the dictionary.
Searching the board will be of the same order as the dictionary since we are not really searching words that are not in the dictionary. But in reality it will be more work than that as we still need to backtrack along the board to construct new words until we can consult the dictionary prefix tree to know whether it exists or not.

Third and Final Solution - No search space + Dynamic Programming:

The 2nd approach mentioned above was good enough until the board size was 5. Unfortunately with a board size of 6, that too was taking forever to complete!



It got me into thinking - "Dang, this search space is still too big to search! Can I just get rid of it entirely?" And then this idea popped into my mind: instead of random constructing word after word in this infinite ocean of words why don't I take a word from the dictionary and somehow magically check whether that's available on the board or not?

It turns out, we can use a nifty dynamic programming technique to quickly check whether a word (from the dictionary in this case) can be constructed from the board or not!

Here is core point of the dynamic programming idea:


For a word of length k to be found (end location) at the [i, j]-th location of the board, the k-1'th letter of that word must be located in one of the adjacent cells of [i, j].



The base case is k = 1.

A letter of length 1 will be found (end location) in the [i, j]-th cell of the board of the only letter in the word matches the letter in the [i, j]-th location of the board.

Once our dynamic programming table is populated with the base case, we can build on top of that for any word of length k, k > 1.

Here is a sample code for this:

for (k = 2; k < MAX_WORD_LENGTH; ++k)
    for (i = 0; i < N; ++i)
        for (j = 0; j < N; ++j)
            if (board[i][j] == word[k])
            {
                 for all the "adjacent" cells of board[i, j]
                     if we table[k-1][adjacent_i][adjacent_j] is true
                         then table[k][i][j] = true;
             }


*I have the C++ code, if you are interested to take a look leave a comment with your email address.

Run Time Complexity:

The run time complexity of this approach is obvious is pretty obvious. It's O (W * N * N * MAX_WORD_LENGTH). Here N is dimension of the board which is usually between 4 to 6. So essentially the algorithm runs in the order of the size of the dictionary!

I solved a 6 X 6 boggle game with a dictionary with 600,000 words in 0.002 seconds! With I/O it as about 0.57 seconds. But with the trie and binary search approaches, it took much longer!

So, here is the summary of my a weekend's worth of adventure in to the world of algorithms and data structures. Feel free to ask me any questions, or any bug in these algorithms:)

Credit where it is due: My friend Satej who is a ninja problem solver and a PhD student at the UCF explained the dynamic programming solution to me first before I refined and finalized it. Thanks Satej!


Update (5/10/2012):

So many people have asked for the source code that I feel I better add the C++ code here:) The final, polished code for this is not in my home computer here, so if there is a bug please let me know!

#include <cstdio>
#include <iostream>

using namespace std;

const int N = 6; // max length of a word in the board

char in[N * N + 1]; // max length of a word
char board[N+1][N+2]; // keep room for a newline and null char at the end
char prev[N * N + 1];
bool dp[N * N + 1][N][N];

// direction X-Y delta pairs for adjacent cells
int dx[] = {0, 1, 1, 1, 0, -1, -1, -1};
int dy[] = {1, 1, 0, -1, -1, -1, 0, 1};
bool visited[N][N];

bool checkBoard(char* word, int curIndex, int r, int c, int wordLen)
{
    if (curIndex == wordLen - 1)
    {
        //cout << "Returned TRUE!!" << endl;
        return true;
    }
   
    int ret = false;
       
    for (int i = 0; i < 8; ++i)
    {
        int newR = r + dx[i];
        int newC = c + dy[i];
       
        if (newR >= 0 && newR < N && newC >= 0 && newC < N && !visited[newR][newC] && word[curIndex+1] == board[newR][newC])
        {
            ++curIndex;
            visited[newR][newC] = true;
           
            ret = checkBoard(word, curIndex, newR, newC, wordLen);
            if (ret)
                break;
               
            --curIndex;
            visited[newR][newC] = false;
        }
    }
   
    return ret;           
}

int main(int argc, char* argv[])
{
   
    int i, j, k, l;
   
    FILE* fDict = fopen("dict.txt","r");
    FILE* fBoard = fopen("tmp2.txt","r");
   
    for(i = 0; i < N; ++i)
        fgets(board[i], N+2, fBoard);
   
    strcpy(prev,"");
    int pLen = 0;
   
    while(fgets(in, N*N + 1, fDict))
    {
        int len = strlen(in);
        if (in[len-1] == '\n')
        {
            in[len-1] = '\0'; // remove the trailing newline
            --len;
        }
       
        if (len < 3)
            continue; // we only want words longer than 3 or more letter
           
        for(i = 0; i < len && i < pLen; ++i)
        {
            if(prev[i] != in[i])
                break;
        }
       
        int firstMismatch = i; // little optimization: building on previous word (will benefit if the word list is sorted)
       
        if(firstMismatch==0)
        {
            for(i = 0; i < 6; ++i)
            {
                for(j = 0; j < 6; ++j)
                {
                    if(board[i][j] == in[0])
                        dp[0][i][j] = true;
                    else
                        dp[0][i][j] = false;
                }
            }
            firstMismatch = 1;
        }
       
        for(k = firstMismatch; k < len; ++k)
        {
            for(i=0;i<6;++i)
            {
                for(j=0;j<6;++j)
                {   
                    dp[k][i][j] = false;
                           
                    if(board[i][j] != in[k])
                        continue;
                       
                    for(l= 0; l < 8 && !dp[k][i][j]; ++l)
                    {
                        int ti = i + dx[l];
                        int tj = j + dy[l];
                       
                        if(ti < 0 || ti >= 6 || tj < 0 || tj >= 6)
                            continue;
                       
                        if (dp[k-1][ti][tj])
                            dp[k][i][j] = true;
                    }
                }
            }  
        }
       
        // check if the word is tagged as found in the dp table
        bool flag = false;
        for(i = 0; i < 6 && !flag; ++i)
        {
            for(j = 0; j < 6 && !flag; ++j)
            {
                if(dp[len-1][i][j])
                    flag =true;
            }
        }
       
        // dp table says its there, but make sure its in the board and it does not repeat a location in the board
        if(flag)
        {
            //cout << "Checking word: " << in << endl;
            bool verified = false;
           
            for (i = 0; i < N && !verified; ++i)
            {
                for (j = 0; j < N && !verified; ++j)
                {
                    if (in[0] != board[i][j])
                        continue;
                       
                    memset(visited, false, sizeof(visited));
                    visited[i][j] = true;
                   
                    if (checkBoard(in, 0, i, j, len))
                    {
                        cout << in << endl;
                        break;
                    }
                }
            }
        }
       
        strcpy(prev,in);
        pLen=len;
           
    }
   
    return 0;
}

Enjoy!

Update 3/15/2015: I have added a Java version of the boggle game solution on my Github page here: https://github.com/bilash/boggle-solver

The Java version builds a Trie for the dictionary and then uses the dynamic programming approach mentioned above.