An automated technique for drafting territories in the board game risk

Richard Gibson, Neesha Desai, Richard Zhao

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

1 Scopus citations

Abstract

In the standard rules of the board game Risk, players take turns selecting or "drafting" the 42 territories on the board until all territories are owned. We present a technique for drafting territories in Risk that combines theMonte Carlo tree search algorithm UCT with an automated evaluation function. Created through supervised machine learning, this function scores outcomes of drafts in order to shorten the length of a UCT simulation. Using this approach, we augment an existing bot for the computer game Lux Delux, a clone of Risk. Our drafting technique is shown to greatly improve performance against the strongest opponents supplied with Lux Delux. The evidence provided indicates that territory drafting is important to overall success in Risk.

Original languageEnglish (US)
Title of host publicationProceedings of the 6th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2010
Pages15-20
Number of pages6
StatePublished - Dec 1 2010
Event6th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2010 - Stanford, CA, United States
Duration: Oct 11 2010Oct 13 2010

Publication series

NameProceedings of the 6th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2010

Other

Other6th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2010
CountryUnited States
CityStanford, CA
Period10/11/1010/13/10

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Visual Arts and Performing Arts

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