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# Combining KR and search: Crossword puzzles PowerPoint PPT Presentation

Combining KR and search: Crossword puzzles. Next: Logic representations Reading: C. 7.4-7.8. Changes in Homework. Mar 4 th : Hand in written design, planned code for all modules Mar 9 th : midterm Mar 25 th : Fully running system due Mar 30 th : Tournament begins. Changes in Homework.

Combining KR and search: Crossword puzzles

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## Combining KR and search: Crossword puzzles

Next: Logic representations

### Changes in Homework

• Mar 4th: Hand in written design, planned code for all modules

• Mar 9th: midterm

• Mar 25th: Fully running system due

• Mar 30th: Tournament begins

### Changes in Homework

• Dictionary

• Use dictionary provided; do not use your own

• Switch to larger set by time of tournament

• Representation of dictionary is important to reducing search time

• Using knowledge to generate word candidates could also help

• ### Midterm Survey

• Start after 9AM Friday and finish by Thursday, Mar. 4th

### Crossword Puzzle Solver

• Proverb: Michael Litman, Duke Univ

• Developed by his AI class

• Combines knowledge from multiple sources to solve clues (clue/target)

• Uses constraint propogation in combination with probabilities to select best target

• ### Algorithm Overview

• Independent programs specialize in different types of clues – knowledge experts

• Information retrieval, database search, machine learning

• Each expert module generates a candidate list (with probabilities)

• Centralized solver

• Merges the candidates lists for each clue

• Places candidates on the puzzle grid

• ### Performance

• Averages 95.3% words correct and 98.1% letters correct

• Under 15 minutes/puzzle

• Tested on a sample of 370 NYT puzzles

• Misses roughly 3 words or 4 letters on a daily 15X15 puzzle

### Questions

• Is this approach any more intelligent than the chess playing programs?

• Does the use of knowledge correspond to intelligence?

• Do any of the techniques for generating words apply to Scrabble?

### To begin: research style

• Study of existing puzzles

• How hard?

• What are the clues like?

• What sources of knowledge might be helpful?

• Crossword Puzzle database (CWDB)

• 350,000 clue-target pairs

• >250,000 unique pairs

• = # of puzzles seen over 14 years at rate of one puzzle/day

• ### How novel are crossword puzzles?

• Given complete database and a new puzzle, expect to have seen

• 91% of targets

• 50% of clues

• 34% of clue target pairs

• 96% of individual words in clues

### Categories of clues

• Fill in the blank:

• 28D: Nothing ____: less

• Trailing question mark

• 4D: The end of Plato?:

• Abbreviations

• 55D: Key abbr: maj

• ### Expert Categories

• Synonyms

• Kind-of

• 27D Kind of coal or coat: pea

• “pea coal” and “pea coat” standard phrases

• Movies

• 50D Princess in Woolf’s “Orlando”: sasha

• Geography

• 59A North Sea port: aberdeen

• Music

• 2D “Hold Me” country Grammay winner, 1988: oslin

• Literature

• 53A Playwright/novelist Capek: karel

• Information retrieval

• 6D Mountain known locally as Chomolungma: everest

• ### Candidate generator

• Farrow of “Peyton Place”: mia

• Movie module returns:

• 0.909091 mia

• 0.010101 tom

• 0.010101 kip

• 0.010101 ben

• 0.010101 peg

• 0.010101 ray

### Ablation tests

• Removed each module one at a time, rerunning all training puzzles

• No single module changed overall percent correct by more than 1%

• Removing all modules that relied on CWDB

• 94.8% to 27.1% correct

• Using only the modules that relied exclusively on CWDB

• 87.6% correct

• ### Word list modules

• WordList, WordListBig

• Ignore their clues and return all words of correct length

• WordList

• 655,000 terms

• WordListBig

• WordList plus constructed terms

• First and last names, adjacent words from clues

• 2.1 million terms, all weighted equally

• 5D 10,000 words, perhaps: novelette

• Wordlist-CWDB

• 58,000 unique targets

• Returns all targets of appropriate length

• Weights with estimates of their “prior” probabilities as targets of arbitrary clues

• Examine frequency in crossword puzzles and normalize to account for bias caused by letters intersecting across and down terms

• ### CWDB-specific modules

• Exact Match

• Returns all targets of the correct length associated with the clue

• Example error: it returns eeyore for 19A Pal of Pooh: tigger

• Transformations

• Learns transformations to clue-target pairs

• Single-word substitution, remove one phrase from beginning or end and add another, depluralizing a word in clue, pluralize word in target

• Nice X <-> X in France

• X for short <-> X abbr.

• X start <-> Prefix with X

• X city <-> X capital

• 51D: Bugs chaser: elmer, solved by Bugs pursuer: elmer and the transformation rule X pursuer <-> X chaser

• http://www.oneacross.com

• ### Information retrieval modules

• Encyclopedia

• For each query term, compute distribution of terms “close” to query

• Counted 10-k times every times it apears at a distance of k<10 from query term

• Extremely common terms (as, and) are ignored

• Partial match

• For a clue c, find all clues in CWDB that share words

• For each such clue, give its target a weight

• LSI-Ency, LSI-CWDB

• Latent semantic indexing (LSI) identifies correlations between words: synonyms

• Return closest word for each word in the clue

• ### Database Modules

• Movie

• www.imdb.com

• Looks for patterns in the clue and formulates query to database

• Quoted titles: 56D “The Thief of Baghdad” role: abu

• Boolean operations: Cary or Lee: grant

• Music, literary, geography

• Simple pattern matching of clue (keywords “city”, “author”, “band”, etc) to formulate query

• 15A “Foundation of Trilogy” author: asimov

• Geography database: Getty Information Institute

• ### Synonyms

• WordNet

• Look for root forms of words in the clue

• Then find variety of related words

• 49D Chop-chop: apace

• Synonyms of synonyms

• Forms of related words converted to forms of clue word (number, tense)

• 18A Stymied: thwarted

• Is this relevant to Scrabble?

### Syntactic Modules

• Fill-in-the-blanks

• >5% clues

• Search databases (music, geography, literary and quotes) to find clue patterns

• 36A Yerby’s “A Rose for _ _ _ Maria”: ana

• Pattern: for _ _ _ Maria

• Allow any 3 characters to fill the blanks

• Kindof

• Pattern matching over short phrases

• 50 clues of this type

• “type of” (A type of jacket: nehru)

• “starter for” (Starter for saxon: anglo)

• “suffix with” (Suffix with switch or sock: eroo

• ### Implicit Distribution Modules

• Some targets not included in any database, but more probable than random

• Schaeffer vs. srhffeeca

• Bigram module

• Generates all possible letter sequences of the given length by returning a letter bigram distribution over all possible strings, learned from CWDB

• Lowest probability clue-target, but higher probability than random sequence of letters

• Honolulu wear: hawaiianmuumuu

• How could this be used for Scrabble?

• ### Questions

• Is this approach any more intelligent than the chess playing programs?

• Does the use of knowledge correspond to intelligence?

• Do any of the techniques for generating words apply to Scrabble?