Using association rule mining to predict opponent deck content in android: Netrunner

Nick Sephton, Peter I. Cowling, Sam Devlin, Victoria J. Hodge, Nicholas H. Slaven

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

Abstract

As part of their design, card games often include information that is hidden from opponents and represents a strategic advantage if discovered. A player that can discover this information will be able to alter their strategy based on the nature of that information, and therefore become a more competent opponent. In this paper, we employ association rule-mining techniques for predicting item multisets, and show them to be effective in predicting the content of Netrunner decks. We then apply different modifications based on heuristic knowledge of the Netrunner game, and show the effectiveness of techniques which consider this knowledge during rule generation and prediction.

Original languageEnglish
Title of host publication2016 IEEE Conference on Computational Intelligence and Games, CIG 2016
PublisherIEEE Computer Society
ISBN (Electronic)9781509018833
DOIs
Publication statusPublished - 20 Sep 2016
Event2016 IEEE Conference on Computational Intelligence and Games, CIG 2016 - Santorini, Greece
Duration: 20 Sep 201623 Sep 2016

Conference

Conference2016 IEEE Conference on Computational Intelligence and Games, CIG 2016
Country/TerritoryGreece
CitySantorini
Period20/09/1623/09/16

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