Automated Discovery of Search-Extension Features

One of the main challenges with selective search extensions is designing effective move categories (features). This 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. In this work we introduce 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 two orders of magnitude fewer combinations than a brute force method does, while preserving good precision and recall.

• Type: Conference
• Year: 2009
• Where: ACG
• Authors: Pálmi Skowronski, Yngvi Björnsson, Mark H. M. Winands
• Keywords: ACG 2009, Computer Graphics, Expert Human Knowledge, Gradual Focus, Selective Search Extensions