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Fall 2023
In this thesis, we investigate applying deep learning techniques to learn the win-loss-draw results contained in the databases of the checkers-playing program CHINOOK. Our initial objectives were to (1) compare a deep-learning-based compression scheme versus the custom algorithm used in CHINOOK,...
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Fall 2024
This thesis empirically investigates the comparative ease of learning policies and heuristics for bidirectional versus unidirectional search in satisficing classical planning. Our research explores the potential advantages of bidirectional search in terms of learnability and efficiency of the...