POMCP with Human Preferences in Settlers of Catan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

We present a suite of techniques for extending the Partially Observable Monte Carlo Planning algorithm to handle complex multi-agent games. We design the planning algorithm to exploit the inherent structure of the game. When game rules naturally cluster the actions into sets called types, these can be leveraged to extract characteristics and high-level strategies from a sparse corpus of human play. Another key insight is to account for action legality both when extracting policies from game play and when these are used to inform the forward sampling method. We evaluate our algorithm against other baselines and versus ablated versions of itself in the well-known board game Settlers of Catan.
Original languageEnglish
Title of host publicationProceedings of the Fourteenth Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE 2018)
Place of PublicationUniversity of Alberta, Edmonton, AB, Canada
PublisherAAAI Press
Number of pages7
Publication statusPublished - 2018
EventThe 14th AAAI Conference on Artificial Intelligence and
- University of Alberta, Edmonton, Canada
Duration: 13 Nov 201817 Nov 2018

Publication series

PublisherAAAI Press
ISSN (Print)2326-909X
ISSN (Electronic)2334-0924


ConferenceThe 14th AAAI Conference on Artificial Intelligence and
Abbreviated titleAIIDE 2018
Internet address


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