By the same authors

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

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

Standard

Using association rule mining to predict opponent deck content in android : Netrunner. / Sephton, Nick; Cowling, Peter I.; Devlin, Sam; Hodge, Victoria J.; Slaven, Nicholas H.

2016 IEEE Conference on Computational Intelligence and Games, CIG 2016. IEEE Computer Society Press, 2016. 7860399.

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

Harvard

Sephton, N, Cowling, PI, Devlin, S, Hodge, VJ & Slaven, NH 2016, Using association rule mining to predict opponent deck content in android: Netrunner. in 2016 IEEE Conference on Computational Intelligence and Games, CIG 2016., 7860399, IEEE Computer Society Press, 2016 IEEE Conference on Computational Intelligence and Games, CIG 2016, Santorini, Greece, 20/09/16. https://doi.org/10.1109/CIG.2016.7860399

APA

Sephton, N., Cowling, P. I., Devlin, S., Hodge, V. J., & Slaven, N. H. (2016). Using association rule mining to predict opponent deck content in android: Netrunner. In 2016 IEEE Conference on Computational Intelligence and Games, CIG 2016 [7860399] IEEE Computer Society Press. https://doi.org/10.1109/CIG.2016.7860399

Vancouver

Sephton N, Cowling PI, Devlin S, Hodge VJ, Slaven NH. Using association rule mining to predict opponent deck content in android: Netrunner. In 2016 IEEE Conference on Computational Intelligence and Games, CIG 2016. IEEE Computer Society Press. 2016. 7860399 https://doi.org/10.1109/CIG.2016.7860399

Author

Sephton, Nick ; Cowling, Peter I. ; Devlin, Sam ; Hodge, Victoria J. ; Slaven, Nicholas H. / Using association rule mining to predict opponent deck content in android : Netrunner. 2016 IEEE Conference on Computational Intelligence and Games, CIG 2016. IEEE Computer Society Press, 2016.

Bibtex - Download

@inproceedings{eb794bd4373b4cf68d183b3698bb121e,
title = "Using association rule mining to predict opponent deck content in android: Netrunner",
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.",
author = "Nick Sephton and Cowling, {Peter I.} and Sam Devlin and Hodge, {Victoria J.} and Slaven, {Nicholas H.}",
year = "2016",
month = sep,
day = "20",
doi = "10.1109/CIG.2016.7860399",
language = "English",
booktitle = "2016 IEEE Conference on Computational Intelligence and Games, CIG 2016",
publisher = "IEEE Computer Society Press",
note = "2016 IEEE Conference on Computational Intelligence and Games, CIG 2016 ; Conference date: 20-09-2016 Through 23-09-2016",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Using association rule mining to predict opponent deck content in android

T2 - 2016 IEEE Conference on Computational Intelligence and Games, CIG 2016

AU - Sephton, Nick

AU - Cowling, Peter I.

AU - Devlin, Sam

AU - Hodge, Victoria J.

AU - Slaven, Nicholas H.

PY - 2016/9/20

Y1 - 2016/9/20

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85015448071&partnerID=8YFLogxK

U2 - 10.1109/CIG.2016.7860399

DO - 10.1109/CIG.2016.7860399

M3 - Conference contribution

BT - 2016 IEEE Conference on Computational Intelligence and Games, CIG 2016

PB - IEEE Computer Society Press

Y2 - 20 September 2016 through 23 September 2016

ER -