Singular extensions: adding selectivity to brute-force searching
Artificial Intelligence - Special issue on computer chess
Automatic feature generation for problem solving systems
ML92 Proceedings of the ninth international workshop on Machine learning
From Simple Features to Sophisticated Evaluation Functions
CG '98 Proceedings of the First International Conference on Computers and Games
Selective depth-first game-tree search
Selective depth-first game-tree search
Learning extension parameters in game-tree search
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Heuristic search and computer game playing III
Feature construction for reinforcement learning in hearts
CG'06 Proceedings of the 5th international conference on Computers and games
RSPSA: enhanced parameter optimization in games
ACG'05 Proceedings of the 11th international conference on Advances in Computer Games
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One of the main challenges with selective search extensions is designing effective move categories (features). Usually, it is a manual trial-and-error task, which requires both intuition and expert human knowledge. Automating this task potentially enables the discovery of both more complex and more effective move categories. The current work introduces Gradual Focus, an algorithm for automatically discovering interesting move categories for selective search extensions. The algorithm iteratively creates new more refined move categories by combining features from an atomic feature set. Empirical data is presented for the game Breakthrough showing that Gradual Focus looks at a number of combinations that is two orders of magnitude fewer than a brute-force method does, while preserving adequate precision and recall.