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

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

Computer Science

Research output: Contribution to conference › Paper › peer-review

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 language	English
Publication status	Published - 20 Sept 2016
Event	IEEE Computational Intelligence and Games Conference (CIG 2016) - Greece, Santorini, United Kingdom Duration: 20 Sept 2016 → 23 Sept 2016

Conference

Conference	IEEE Computational Intelligence and Games Conference (CIG 2016)
Country/Territory	United Kingdom
City	Santorini
Period	20/09/16 → 23/09/16

Access to Document

2016_CIG_RuleMiningFinal published version, 506 KB

The Digital Creativity Hub
Cowling, P. I., Austin, J., Cairns, P. A., Holliman, N. S., Hook, J. D., Marsden, E., Murphy, D. T., Petrie, H., Reed, D. J., Richards, J. D., Ursu, M., Wade, A., Baier, H., Beale, G., Block, F. O., Deterding, C. S., Devlin, S., Drachen, A., Kasprowicz, R. E., Smith, D. P. & Williams, D. A. H.
EPSRC
1/10/15 → 30/09/22
Project: Research project (funded) › Research
New Economic Models & Opportunities for Digital Games
Cowling, P. I., Cabras, I., Fernandes, K. J., Kudenko, D. & Devlin, S.
EPSRC
14/10/13 → 13/10/16
Project: Research project (funded) › Research

Cite this

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title = "Using Association Rule Mining to Predict Opponent Deck Content in Android: Netrunner",

abstract = "As part of their design, card games often includeinformation that is hidden from opponents and represents astrategic advantage if discovered. A player that can discoverthis information will be able to alter their strategy based onthe nature of that information, and therefore become a morecompetent opponent. In this paper, we employ association rule-miningtechniques for predicting item multisets, and show themto be effective in predicting the content of Netrunner decks. Wethen apply different modifications based on heuristic knowledgeof the Netrunner game, and show the effectiveness of techniqueswhich consider this knowledge during rule generation andprediction.",

author = "Sephton, {Nicholas John} and Cowling, {Peter Ivan} and Sam Devlin and Hodge, {Victoria Jane} and Slaven, {Nicholas H}",

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note = "IEEE Computational Intelligence and Games Conference (CIG 2016) ; Conference date: 20-09-2016 Through 23-09-2016",

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AU - Sephton, Nicholas John

AU - Cowling, Peter Ivan

AU - Devlin, Sam

AU - Hodge, Victoria Jane

AU - Slaven, Nicholas H

PY - 2016/9/20

Y1 - 2016/9/20

N2 - As part of their design, card games often includeinformation that is hidden from opponents and represents astrategic advantage if discovered. A player that can discoverthis information will be able to alter their strategy based onthe nature of that information, and therefore become a morecompetent opponent. In this paper, we employ association rule-miningtechniques for predicting item multisets, and show themto be effective in predicting the content of Netrunner decks. Wethen apply different modifications based on heuristic knowledgeof the Netrunner game, and show the effectiveness of techniqueswhich consider this knowledge during rule generation andprediction.

AB - As part of their design, card games often includeinformation that is hidden from opponents and represents astrategic advantage if discovered. A player that can discoverthis information will be able to alter their strategy based onthe nature of that information, and therefore become a morecompetent opponent. In this paper, we employ association rule-miningtechniques for predicting item multisets, and show themto be effective in predicting the content of Netrunner decks. Wethen apply different modifications based on heuristic knowledgeof the Netrunner game, and show the effectiveness of techniqueswhich consider this knowledge during rule generation andprediction.

M3 - Paper

T2 - IEEE Computational Intelligence and Games Conference (CIG 2016)

Y2 - 20 September 2016 through 23 September 2016

ER -

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

Abstract

Conference

Access to Document

Projects

The Digital Creativity Hub

New Economic Models & Opportunities for Digital Games

Cite this