By the same authors

Like a DNA string: Sequence-based Player Profiling in Tom Clancy’s The Division

Research output: Contribution to conferencePaper

Standard

Like a DNA string : Sequence-based Player Profiling in Tom Clancy’s The Division. / Makarovych, Sasha ; Canossa, Alessandro; Togelius, Julian; Drachen, Anders.

2018. Paper presented at Artificial Intelligence and Interactive Digital Entertainment Conference, .

Research output: Contribution to conferencePaper

Harvard

Makarovych, S, Canossa, A, Togelius, J & Drachen, A 2018, 'Like a DNA string: Sequence-based Player Profiling in Tom Clancy’s The Division' Paper presented at Artificial Intelligence and Interactive Digital Entertainment Conference, 13/11/18 - 17/11/18, .

APA

Makarovych, S., Canossa, A., Togelius, J., & Drachen, A. (Accepted/In press). Like a DNA string: Sequence-based Player Profiling in Tom Clancy’s The Division. Paper presented at Artificial Intelligence and Interactive Digital Entertainment Conference, .

Vancouver

Makarovych S, Canossa A, Togelius J, Drachen A. Like a DNA string: Sequence-based Player Profiling in Tom Clancy’s The Division. 2018. Paper presented at Artificial Intelligence and Interactive Digital Entertainment Conference, .

Author

Makarovych, Sasha ; Canossa, Alessandro ; Togelius, Julian ; Drachen, Anders. / Like a DNA string : Sequence-based Player Profiling in Tom Clancy’s The Division. Paper presented at Artificial Intelligence and Interactive Digital Entertainment Conference, .

Bibtex - Download

@conference{6e0bf5e624db4b9782a9f733fa1b2162,
title = "Like a DNA string: Sequence-based Player Profiling in Tom Clancy’s The Division",
abstract = "In this paper we present an approach for using sequence analysisto model player behavior. This approach is designedto work in game development contexts, integrating productionteams and delivering profiles that inform game design.We demonstrate the method via a case study of the gameTom Clancy’s The Division, which with its 20 million playersrepresents a major current commercial title. The approachpresented provides a mixed-methods framework, combiningqualitative knowledge elicitation and workshops with largescaletelemetry analysis, using sequence mining and clusteringto develop detailed player profiles showing the core gameplayloops of The Division’s players.",
author = "Sasha Makarovych and Alessandro Canossa and Julian Togelius and Anders Drachen",
note = "{\circledC} 2018, Association for the Advancement of Artificial Intelligence.; Artificial Intelligence and Interactive Digital Entertainment Conference ; Conference date: 13-11-2018 Through 17-11-2018",
year = "2018",
language = "English",
url = "https://sites.google.com/ncsu.edu/aiide-2018/",

}

RIS (suitable for import to EndNote) - Download

TY - CONF

T1 - Like a DNA string

T2 - Sequence-based Player Profiling in Tom Clancy’s The Division

AU - Makarovych, Sasha

AU - Canossa, Alessandro

AU - Togelius, Julian

AU - Drachen, Anders

N1 - © 2018, Association for the Advancement of Artificial Intelligence.

PY - 2018

Y1 - 2018

N2 - In this paper we present an approach for using sequence analysisto model player behavior. This approach is designedto work in game development contexts, integrating productionteams and delivering profiles that inform game design.We demonstrate the method via a case study of the gameTom Clancy’s The Division, which with its 20 million playersrepresents a major current commercial title. The approachpresented provides a mixed-methods framework, combiningqualitative knowledge elicitation and workshops with largescaletelemetry analysis, using sequence mining and clusteringto develop detailed player profiles showing the core gameplayloops of The Division’s players.

AB - In this paper we present an approach for using sequence analysisto model player behavior. This approach is designedto work in game development contexts, integrating productionteams and delivering profiles that inform game design.We demonstrate the method via a case study of the gameTom Clancy’s The Division, which with its 20 million playersrepresents a major current commercial title. The approachpresented provides a mixed-methods framework, combiningqualitative knowledge elicitation and workshops with largescaletelemetry analysis, using sequence mining and clusteringto develop detailed player profiles showing the core gameplayloops of The Division’s players.

M3 - Paper

ER -