The Wikipedia Boggle entry explains the game in detail including popular culture references to Boggle. Reviewing the rules on the Wikipedia site will help in understanding the play-of-the-game, but you'll be looking at implementation trade-offs that are independent of game-playing. You can play games based on words that are similar to Boggle online, e.g., Facebook's Scramble and Yahoo! Word Racer both of which are definitely related to Boggle.
Theoretical and practical approaches to word games have formed the basis for The World's Fastest Scrabble Program (Appel and Jacobson) for recognizing Genomic Signatures in DeBruijn Chains (Heath and Pati) and for An efficient interface between continuous speech recognition and language understanding (Oerder and Ney).
Analyzing alternative implementations and algorithms will help inform a better understanding of both work and play, what a great way to build a foundation in understanding how to turn data into information.
In particular you'll be examining alternate implementations of a lexicon or word source. You'll work on a Trie implementation and a compressed Trie that's similar to a Radix Trie/Patricia Trie, but simpler to implement. Tries are the data structure behind predictive text used in cell-phone texting, e.g., in T9 Text Input and have formed the basis for IP-routing, e.g., see Keith Sklower's Tree-Based Packet Routing Table for Berkeley Unix although changes to IPv6 may change the utility of tries as described here: Scalable high speed IP routing lookups (Waldvogel, Varghese, Turner, Plattner).
You'll use different word-sources or lexicons. Apparently there's a controversy as to what the "best" lexicon is for playing word games like Scrabble® and Boggle®
As part of Lab 9, you wrote classes that implement
ILexicon interface. Each lexicon implementation
facilitates looking up a word or a possible
prefix of a word. The classes differ in how efficiently they support
these operations and in how much memory they use.
As part of Lab 10, you will design and
code one class that implements the
interface to find a word on a Boggle board. You'll use this class in
implementing one of the auto-player classes and in determining where a
word appears on a board when you or another (human) player plays the
You'll write classes that implement the
interface. Each class facilitates playing a game of Boggle by finding
every possible word on a given Boggle board. These classes use the
lexicons from the previous hierarchy.
You'll make conclusions based on empirical, runtime analyses of the classes you write to determine which of several methods is "best", and to reason in general about the trade-offs in different implementations.
Details about the classes you write for this part of the assignment and help in writing them are provided in the howto pages for implementing lexicons.
You'll design and code three classes for this part of the assignment and you'll
analyze their performance empirically. You're given a JUnit testing class,
TestLexicon, to test your
implementations --- see the howto pages for
help/reminders on using JUnit. In the
load methods you write you
can assume no duplicate words will be inserted via either the Scanner or
ArrayList parameters to the load methods. You're also given a class
LexiconBenchmark you can use
to analyze the empirical performance of your implementations.
In Boggle®, legal words are those found in the lexicon associated with a game. A lexicon is simply a list of words, it's not a dictionary because it doesn't have associated definitions for each word.
The ILexicon interface specifies the
methods exported by the interface which implementations must
provide. You're given an implementation
with an O(n) implementation of the method
wordStatus since the implementation simply does a linear
search over the list of words it stores. Read the
documentation for details of the interface.
You must write two new, more efficient implementations and modify
SimpleLexicon so that it's
executes more efficiently.
BadWordOnBoardFinderthat implements the
IWordOnBoardFinderinterface. The bad word finder returns an empty list of
BoardCellobjects for any query so no words will be found.
You'll implement the method
cellsForWord in a class
a standard, backtracking search to find a word on a Boggle board.
Details and more specifics for this part of the assignment are provided
in the howto pages in the section
IWordOnBoardFinder interface. Those pages
also include hints on how to write the class described here.
Details about the classes you write for this part of the assignment and help in writing them are provided in the howto pages for implementing AutoPlayers.
You'll implement two classes that find all the words on a Boggle board.
Each class uses a different technique and you'll analyze the runtime
tradeoffs in these techniques. You'll also reason empirically about the
performance of these two classes when they're configured with different
BoardFirstAutoPlayer looks at the board and
tries to form words by trying all the paths on the board. The code will
be very similar to the backtracking code you wrote for the
GoodWordOnBoardFinder class, but you'll prune searches
early based on prefixes as described in the howto pages. Another class
LexiconFirstAutoPlayer will be relatively simple to
implement since you'll have working lexicons and a working
WordOnBoardFinder from earlier in this assignment. In
LexiconFirstAutoPlayer you'll write code
to look up every word in the lexicon to see if it's on the board. This
method is surprisingly fast enough for a game of Boggle , but it's
probably not fast enough to run 10,000 times without waiting for a
You need to report in your README file information about which Lexicon implementation is fastest. You should compare all the lexicons and report on their relative times --- you shouldn't simply say "this one is fastest". You should have at least three lexicons to test. You should explain the methods you used to determine this and report on experiments you run, giving times.
You must write code to play lots of auto-games, not games in which
humans play, but games in which all words are found on thousands of
boards --- see
BoggleStats for a starting
You must provide a board that scores the highest of
all the 4x4 and 5x5 boards you test in running at least 10,000
and preferably 50,000 games. Report on how many seconds it takes your
to run for 1,000 games; for 10,000 games (or predict that if
you can't run that many); and predict/justify on
how much time it would take your computer and implementation to
simulate both 100,000 games and one million games. When doing the
be sure to set the random-number generator's seed, currently done
BoggleStats and described in
the howto pages. If you can't run 10,000 auto-games,
indicate the maximum number you can run.
Your README file should list your testing results, all the people with whom you collaborated, and the TAs/UTAs you consulted with. You may want to include your testing and analysis results in a separate analysis document.
You should include an estimate of how long you spent on the program and what your thoughts are about the assignment.
Submit your README and all of your source code using Eclipse with assignment name boggle.