A bottom-up method for developing a trait-based model of player behavior

Mikhail A Kabakov, Alessandro Canossa, Magy Seif El-nasr, Jeremy B Badler, Randy C Colvin, Stefanie Tignor, Zhengxing Chen, Kunal Asarsa

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

Abstrakt

nderstanding player behavior through telemetry logs is an important yet unresolved problem. Interpreting the meaning of players' low-level behaviors over time is important due to its utility in (a) developing a more adaptive and personalized game experience, (b) uncovering game design issues, and (c) understanding the human cognitive processes in a gaming context, not to mention its use and application to learning, training, and health. In this paper, the authors describe a work in progress developing a quantified model of player behavior for interpreting telemetry data from a first-person roll-playing game (RPG). This kind of model constitutes a grammar that will allow us to make sense of low-level behavioral data to assess personality, decision-making, and other cognitive constructs through behavioral measures.
OriginalsprogEngelsk
TitelProceedings of the first ACM SIGCHI Annual Symposium on Computer-Human Interaction in Play
Antal sider2
ForlagAssociation for Computing Machinery
Publikationsdato2014
Sider427-428
DOI
StatusUdgivet - 2014
Udgivet eksterntJa
BegivenhedFirst ACM SIGCHI annual symposium on Computer-human interaction in play -
Varighed: 1 okt. 20143 okt. 2014

Konference

KonferenceFirst ACM SIGCHI annual symposium on Computer-human interaction in play
Periode01/10/201403/10/2014

Kunstnerisk udviklingsvirksomhed (KUV)

  • Nej

Citationsformater