Sequential halving for partially observable games

Tom Pepels*, Tristan Cazenave, Mark H M Winands

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic


This paper investigates Sequential Halving as a selection policy in the following four partially observable games: Go Fish, Lost Cities, Phantom Domineering, and Phantom Go. Additionally, H-MCTS is studied, which uses Sequential Halving at the root of the search tree, and UCB elsewhere. Experimental results reveal that H-MCTS performs the best in Go Fish, whereas its performance is on par in Lost Cities and Phantom Domineering. Sequential Halving as a at Monte-Carlo Search appears to be the stronger technique in Phantom Go.
Original languageEnglish
Title of host publicationComputer Games
Number of pages14
ISBN (Print)9783319394015
Publication statusPublished - 2016

Publication series

SeriesCommunications in Computer and Information Science

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