1. Artificial Intelligence CIS 342 The College of Saint Rose David Goldschmidt, Ph.D. March 6, 2009

2. Crossword Puzzle Construction Given: – Dictionary of valid words and phrases – Empty crossword grid Problem: – Fill the crossword grid such that all words both across and down are valid – Assign clues

3. Crossword Puzzle Construction Depth-First Search ( DFS ) – Fill in words until a solution is found or a dead-end is encountered – Backtrack from dead-ends – Questions : Where do we start? What word do we fill in next? What backtracking strategies do we use? How do we avoid repetition (boring puzzles)?

4. Crossword Puzzle Construction Optimize the DFS: – Add longer (most constrained) words first – Associate weights with words in dictionary based on frequency of letters Friendly crossword puzzle words include letters: S, R, E, T, D, A, I, L Unfriendly crossword puzzle words include letters: J, Q, X, Z, F, V, W e.g. quiz, fix, jazz, quaff, xylophone, wax

5. Crossword Puzzle Construction Genetic Algorithm ( GA ) – Evolve a solution by crossovers and mutations through many generations – Initial population of crossword grids: Random letters? Random letters based on Scrabble ® frequencies? Random words from dictionary? – Fitness of each grid is number of valid words

6. Solving Crossword Puzzles Given: – Crossword grid – Clues Problem: – Fill the grid such that all words correctly answer the given clues

7. Solving Crossword Puzzles Obtain candidate answers for each clue – Assign a confidence value to each candidate – Are we guaranteed to have the correct answer? Place candidate answers in grid until a solution is found or a dead-end occurs – Which backtracking strategies should we use?

8. Solving Crossword Puzzles P ROVERB — Duke University, 1999 – Modules provide candidate answers from dictionaries, encyclopedias, movie databases, etc. – Module sources a Crossword Puzzle Database of exactly 5142 previously solved puzzles Pivotal in P ROVERB ’s success – Another module generates all combinations of letters (ouch!)

9. Solving Crossword Puzzles Google CruciVerbalist ( GCV )

10. Solving Crossword Puzzles GCV solved 13x13 puzzle with 68 clues – Many clues are fill-in-the-blank or pop-culture clues – Candidate answers obtained from Google results page (top 50) – Solved using 559 Google queries – Queries yielded 68 correct answers 44 correct answers had highest confidence

11. Solving Crossword Puzzles

12. Clue Preprocessing Categorize clues based on text and type of clues: – Fill-in-the-blank clues – Synonyms/Antonyms – “Type of” (or “Kind of”) clues – Abbreviations – Clues with “and” or “or” – Singular or plural – Number of words in answer

13. Clue Preprocessing Translate clues to Google-friendly forms – “To ___ is human”  “To * is human”  “To * * is human” – “Mary ___ little lamb” (2 words)  “Mary * * little lamb” – “___ to Joy” by Beethoven  “* to Joy” by Beethoven  “* * to Joy” by Beethoven

14. Clue Preprocessing Translate clues to Google-friendly forms – Diplomacy  synonyms of Diplomacy – Not dry  opposite of dry  antonyms of dry – Joy  synonyms of Joy

15. Clue Preprocessing Translate clues to Google-friendly forms – Type of dancing [ or Kind of dancing]  * dancing – Second sight (abbr.)  Second sight  abbreviations of Second sight – Superman’s admirer  admirer of Superman

16. Clue Preprocessing Translate clues to Google-friendly forms – Couldn’t move  Could not move  Could opposite of move  Could antonyms of move – Knight or Danson  Knight  Danson

17. Clue Preprocessing Translate clues to Google-friendly forms – Bosley and Arnold  Bosley  Arnold  Append an ‘s’ – Henson, and others [ or Henson, and namesakes]  Henson  Append an ‘s’

18. Results of Google-Querying

19. GCV excels at solving fill-in-the-blank and pop-culture clues – Why? Though results are encouraging, using keyword-based searching is limited – Why?

20. Populating the Crossword Grid Use a Depth-First Search ( DFS ) algorithm: – Fill in the crossword grid based on confidence values of candidate words – At each iteration: Select candidate word with highest confidence value amongst clues not yet placed Attempt to fit candidate word into grid – Halt when a solution is found or a dead-end occurs

21. Populating the Crossword Grid When a dead-end occurs, what do we do? – Backtrack: Remove last word placed in grid Disadvantages? – Backjump: Identify culprit and remove all words back to culprit word Disadvantages?

22. Populating the Crossword Grid When a dead-end occurs, what do we do? – Extricating Backjump: Identify and remove the culprit Disadvantages? – How do we identify the culprit?

23. Extricating Backjumping Assign weights to the squares of the grid – Square weights correspond to confidence values of candidate words placed – e.g. Place TWAIN with confidence value of 10 at 5-Across

24. Extricating Backjumping Weights of interlocking words are multiplied

25. Extricating Backjumping Define grid weight of a word as the sum of each individual square weight – e.g. TWAIN = 100, NOW = 72

26. Extricating Backjumping When a dead-end occurs, the culprit is the word with the lowest grid weight

27. A Sampling of Crossword Puzzles

28. New York Times

29. A Sampling of Crossword Puzzles

30. TV Guide #42

31. A Sampling of Crossword Puzzles

32. TV Guide #63

33. A Sampling of Crossword Puzzles

34. Mensa Kids Puzzle #3

35. Results of Grid Solving

36. Limitations of Keyword-Based Search Google and GCV use keyword-based tricks to artificially improve result sets – Word frequency & proximity to other words – Additional keywords to help direct queries to good candidate answers e.g. synonyms of – Grammatical and structural rearrangements

37. Lack of precision in keyword-based search – Irrelevant results in candidate answer lists – Confidence values based on word frequency produces many false positives – Correct answer is often buried in other mediocre ( and incorrect! ) candidates Limitations of Keyword-Based Search

38. In Conclusion.... Other uses of the Web as an automated information source? – Keyword-based search is insufficient – Lacks the means for machine-interpretable information – Semantic Web