Automated Discovery of Search-Extension Features

Pálmi Skowronski*, Yngvi Bjornsson, Mark H. M. Winands

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

2 Citations (Web of Science)


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.
Original languageEnglish
Title of host publicationAdvances in Computer Games
Subtitle of host publication12th International Conference, ACG 2009, Pamplona Spain, May 11-13, 2009. Revised Papers
EditorsH. Jaap van den Herik, Pieter Spronck
Place of PublicationBerlin, Heidelberg
Number of pages13
ISBN (Print)978-3-642-12993-3
Publication statusPublished - 2010

Publication series

SeriesLecture Notes in Computer Science

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