Solving Crossword Puzzles Using Extended Potts Model
Solving crossword puzzles by computers is a challenging task in artificial intelligence. It requires logical inference and association as well as vocabulary and common sense knowledge. For this task, we present an extension of the Potts model. This model can incorporate various clues for solving puzzles and require less computational cost compared with other existing models.
KeywordsWorld Wide Potts Model Candidate List Random Initialization Linguistic Resource
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- 1.Shazeer, N.M., Littman, M.L., Keim, G.A.: Solving Crossword Puzzles as Probabilistic Constraint Satisfaction. In: Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence, pp. 156–162 (1999)Google Scholar
- 2.Keim, G.A., Shazeer, N., Littman, M.L., Agarwal, S., Cheves, C.M., Fitzgerald, J., Grosland, J., Jiang, F., Pollard, S., Weinmeister, K.: Proverb : The Probabilistic Cruciverbalist. In: Proceedings of the Sixteenth National Conference on Artificial Intelligence, pp. 710–717 (1999)Google Scholar
- 3.Sato, S.: Solving Japanese Crossword Puzzles. IPSJ SIG Notes, NL-147-11, 69–76 (2002) (in Japanese)Google Scholar
- 4.Ernandes, M., Angelini, G., Goli, M.: WebCrow: a WEB-based system for CROssWord solving. In: Proceedings of the Twentieth National Conference of Artificial Intelligence, pp. 1412–1417 (2005)Google Scholar