Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
World-championship-caliber Scrabble
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
GIB: Steps Toward an Expert-Level Bridge-Playing Program
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Algorithms for Sequential Decision Making
Algorithms for Sequential Decision Making
Building Policies for Scrabble
Proceedings of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence
Human-like Heuristics in Scrabble
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
Improving state evaluation, inference, and search in trick-based card games
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Human-like Heuristics in Scrabble
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
A Scrabble Heuristic Based on Probability That Performs at Championship Level
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Strategy patterns prediction model (SPPM)
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Real-time opponent modelling in trick-taking card games
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Framework for Constructive Computer Game toward Empowering the Future Generation
International Journal of Green Computing
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Computers have already eclipsed the level of human play in competitive Scrabble, but there remains room for improvement. In particular, there is much to be gained by incorporating information about the opponent's tiles into the decision-making process. In this work, we quantify the value of knowing what letters the opponent has. We use observations from previous plays to predict what tiles our opponent may hold and then use this information to guide our play. Our model of the oppoent, based on Bayes' theorem, sacrifices accuracy for simplicity and ease of computation. But even with this simplified model, we show significant improvement in play over an existing Scrabble program. These empirical results suggest that this simple approximation may serve as a suitable substitute for the intractable partially observable Markov decision process. Although this work focuses on computer-vs-computer Scrabble play, the tools developed can be of great use in training humans to play against other humans.