Using Association Rule Mining to Predict Opponent Deck Content in Android: Netrunner

Nicholas John Sephton, Peter Ivan Cowling, Sam Devlin, Victoria Jane Hodge, Nicholas H Slaven

Research output: Contribution to conferencePaperpeer-review


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
Original languageEnglish
Publication statusPublished - 20 Sept 2016
EventIEEE Computational Intelligence and Games Conference (CIG 2016) - Greece, Santorini, United Kingdom
Duration: 20 Sept 201623 Sept 2016


ConferenceIEEE Computational Intelligence and Games Conference (CIG 2016)
Country/TerritoryUnited Kingdom

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