Solving Crossword Puzzles with AI: a look at Proverb

What is Proverb? Proverb was developed in 1999 to solve crosswords puzzles Works on American puzzles Cannot beat expert humans Solves a puzzle in 15 minutes Gets over 93% of words correct

Goal: So how does it work? Maximize the number of answers in the crossword puzzle that are the same as the solution. So how does it work?

Searching… Different searches performed depending on the category: abbr, synonym, kind of, pop culture, geography, literature, film… Two stage architecture: 1. Specific modules that generate candidate answers 2. Combines results from the modules

Kinds of Modules Database: movie, music, geography, literary, synonyms, etc Syntactic: fill-in-the-blanks, kind of Word list CWDB specific Information retrieval: encyclopedia, partial match, etc

More about modules… modules are given the clue and the number of letters in the target grid constraints are ignored at this point with the exception of word length the module returns anywhere between 0 and 10,000 possible answers each one has a weighted likelihood or probability that it is the correct solution each module also returns a value that represents its confidence that the answer is part of its list

Clue: Farrow of “Peyton Place” (answer: Mia) Confidence score = 1.0 0.010101 – tom 0.010101 – kip 0.010101 – peg 0.010101 – ray

Training the modules… 30 modules were evaluated with test data that consisted of 5374 clues Measures of performance: How often the correct answer was included in the candidate list The average length of the candidate list The number of times that the correct answer appeared as the #1 candidate Percentage of clues that the module guessed at

Grid Filling Probabilistic Constraint Satisfaction Problem Each box is represented as a variable Must maximize the correct solutions Limitations: confused by creativity in clues only solves American puzzles