Michael L. Littman, Greg A. Keim, and Noam M. Shazeer, Duke University
We attacked the problem of solving crossword puzzles by computer: Given a set of clues and a crossword grid, try to maximize the number of words correctly filled in. Proverb , the probabilistic cruciverbalist, separates the problem into two, more familiar subproblems: candidate generation and grid filling. In candidate generation, each clue is treated as a type of query to an information retrieval system, and relevant words of the correct length are returned along with confidence scores. In grid filling, the candidate words are fit into the puzzle grid to maximize an overall confidence score using a combination of ideas from belief network inference and constraint satisfaction. For our demonstration, we will have an interactive version of the candidate-generation process available via the web, and will also give people an opportunity to go head-to- head against Proverb in solving complete puzzles.