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Categorical Clustering Applied to the Discovery of Character Builds in TCTD2: The BaT Approach
Massive Entertainment, Ubisoft, Malmö.
Massive Entertainment, Ubisoft, Malmö.
Massive Entertainment, Ubisoft, Malmö. (DDS)ORCID iD: 0000-0002-6016-028X
Massive Entertainment, Ubisoft, Malmö.
Show others and affiliations
2020 (English)In: 2020 IEEE Conference on Games (CoG), IEEE, 2020, p. 1-8Conference paper, Published paper (Refereed)
Abstract [en]

This article describes an attempt to categorize char- acter configurations of players of Tom Clancy’s the Division 2, conducted to highlight behavioral differences in approach to gameplay based on one’s character build. Nine distinct character builds were extracted for maximum coherence and minimum variance and each build showed significant differences in separate measures of behavior such as playtime, character health and armor among other attributes. The proposed method was also able to recover builds recognized by social forums as well as discovering new ones. Appropriation of Character builds as categorical text-based data (BaT: Build as Text), provides a unique opportunity for game researchers to use a diverse set of input data which will in turn contribute to the improvement of the process of game design informed by player choices. Longitudinal observations in interconnection of obtained clusters may provide further insight into formation and evolution of gameplay types.

Place, publisher, year, edition, pages
IEEE, 2020. p. 1-8
Series
IEEE Conference on Computational Intelligence and Games, ISSN 2325-4270, E-ISSN 2325-4289
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mau:diva-41434DOI: 10.1109/CoG47356.2020.9231916ISBN: 978-1-7281-4533-4 (electronic)ISBN: 978-1-7281-4534-1 (print)OAI: oai:DiVA.org:mau-41434DiVA, id: diva2:1539880
Conference
IEEE's Conference on Games 2020, Osaka, Japan, 24-27 Aug. 2020
Available from: 2021-03-25 Created: 2021-03-25 Last updated: 2022-05-10Bibliographically approved
In thesis
1. Predictive Psychological Player Profiling
Open this publication in new window or tab >>Predictive Psychological Player Profiling
2021 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Video games have become the largest portion of the entertainment industry and everyday life of millions of players around the world. Considering games as cultural artifacts, it seems imperative to study both games and players to understand underlying psychological and behavioral implications of interacting with this medium, especially since video games are rich domains for occurrence of rich affective experiences annotated by and measurable via in-game behavior. This thesis is a presentation of a series of studies that attempt to model player perception and behavior as well as their psychosocial attributes in order to make sense of interrelations of these factors and implications the findings have for game designers and researchers. In separate studies including survey and in-game telemetry data of millions of players, we delve into reliable measures of player psychological need satisfaction, motivation and generational cohort and cross reference them with in-game behavioral patterns by presenting systemic frameworks for classification and regression. We introduce a measurement of perceived need satisfaction and discuss generational effects in playtime and motivation, present a robust prediction model for ordinally processed motivations and review classification techniques when it comes to playstyles derived from player choices. Additionally, social aspects of play, such as social influence and contagion as well as disruptive behavior, is discussed along with advanced statistical models to detect and explain them.   

Place, publisher, year, edition, pages
Malmö: Malmö universitet, 2021. p. 121
Series
Studies in Computer Science
Keywords
Human-Computer Interaction, Affective Computing, Player Experience, User Research, Behavioral modeling, Psychology of play
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:mau:diva-41436 (URN)
Presentation
2021-05-27, Zoom, 17:00 (English)
Supervisors
Note

Note: The papers are not included in the fulltext online

Vid tidpunkten för disputationen var följande delarbete opublicerat: delarbete I (manuskript).

At the time of the doctoral defence the following paper was unpublished: paper I (manuscript).

Available from: 2021-03-26 Created: 2021-03-25 Last updated: 2024-03-04Bibliographically approved

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Azadvar, Ahmad

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