BibSLEIGH — Anders

Collaborated with:

R.Sifa C.Bauckhage Alessandro Canossa C.Thurau Georgios N. Yannakakis M.Hitchens Julian Runge Fabian Hadiji James Alfred Walker K.Kersting M.Schubert S.Kriglstein Julian Togelius Simon Demediuk Diego Klabjan Valerio Bonometti Alex R. Wade F.Block Janus Rau Møller Sørensen K.Bauer Robert W. D. Veitch G.Wallner Arnav Jhala Nicholas Ross Y.N.Ravari P.Spronck Falko Weigert Petersen Line Ebdrup Thomsen P.Mirza-Babaei Sasha Makarovych J.Pirker Isabel Lesjak Andreas Punz Myat Aung L.E.Nacke Tobias Mahlmann Sridev Srikanth C.Ojeda Peter York Charles Ringer Mark Hall Marinka Copier M.Montola Mirjam P. Eladhari J.Stenros Marco Tamassia William L. Raffe Fabio Zambetta Oliver James Scholten Nathan Gerard Jayy Hughes Sebastian Deterding David Zendle Eric Thurston Lundquist Yungjen Kung Pranav Simha Rao James Green Chester Gray Elie Harik Patty Lu Peter I. Cowling A. V. Kokkinakis C. Yoder Adam Katona Ryan J. Spick V.J.Hodge Mike Schaekermann Giovanni Ribeiro Guenter Wallner Daniel M. Johnson 0001 K.Kuikkaniemi Jörg Niesenhaus H.Korhonen Wouter van den Hoogen Karolien Poels W.A.IJsselsteijn Yvonne A. W. de Kort Y.Sun Ye Tu Yu Ang Siva Nekkanti Shantanu Raghav

Talks about:

game (22) player (14) predict (10) behavior (7) play (7) destini (5) profil (5) cluster (4) model (4) mobil (4)

Person: Anders Drachen

DBLP: Drachen:Anders

Contributed to:

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

Wrote 35 papers:

CIG-2009-DrachenCY #modelling #self #using: Player modeling using self-organization in Tomb Raider: Underworld (AD, AC, GNY), pp. 1–8.
DiGRA-2009-CanossaD #development #game studies: Patterns of Play: Play-Personas in User-Centred Game Development (AC, AD).
DiGRA-2009-DrachenCMEHS #game studies: Role-Playing Games: The State of Knowledge [Panel Abstracts] (AD, MC, MM, MPE, MH, JS).
DiGRA-2009-DrachenHJY #data-driven #towards: Towards Data-Driven Drama Management: Issues in Data Collection and Annotation (AD, MH, AJ, GNY).
DiGRA-2009-NackeDKNKHPIK #experience #research: Playability and Player Experience Research [Panel Abstracts] (LEN, AD, KK, JN, HK, WvdH, KP, WAI, YAWdK).
CIG-2010-MahlmannDTCY #behaviour #predict: Predicting player behavior in Tomb Raider: Underworld (TM, AD, JT, AC, GNY), pp. 178–185.
FDG-2011-CanossaDS #detection #exclamation: Arrrgghh!!!: blending quantitative and qualitative methods to detect player frustration (AC, AD, JRMS), pp. 61–68.
FDG-2011-DrachenBV #game studies #process: Only the good... get pirated: game piracy activity vs. metacritic score (AD, KB, RWDV), pp. 292–294.
CIG-2012-BauckhageKSTDC #empirical #game studies #how: How players lose interest in playing a game: An empirical study based on distributions of total playing times (CB, KK, RS, CT, AD, AC), pp. 139–146.
CIG-2012-DrachenSBT #behaviour #clustering #game studies: Guns, swords and data: Clustering of player behavior in computer games in the wild (AD, RS, CB, CT), pp. 163–170.
CIG-2013-DrachenS #game studies #visualisation: Spatial game analytics and visualization (AD, MS), pp. 1–8.
CIG-2013-SifaDBTC #behaviour #evolution: Behavior evolution in Tomb Raider Underworld (RS, AD, CB, CT, AC), pp. 1–8.
FDG-2013-DrachenTSB #behaviour #clustering #comparison: A comparison of methods for player clustering via behavioral telemetry (AD, CT, RS, CB), pp. 245–252.
CIG-2014-BauckhageSDTH #behaviour #clustering #game studies #heatmap #using: Beyond heatmaps: Spatio-temporal clustering using behavior-based partitioning of game levels (CB, RS, AD, CT, FH), pp. 1–8.
CIG-2014-HadijiSDTKB #predict: Predicting player churn in the wild (FH, RS, AD, CT, KK, CB), pp. 1–8.
CIG-2014-SifaBD #modelling #scalability: The Playtime Principle: Large-scale cross-games interest modeling (RS, CB, AD), pp. 1–8.
AIIDE-2015-SifaDB #analysis #behaviour #scalability: Large-Scale Cross-Game Player Behavior Analysis on Steam (RS, AD, CB), pp. 198–204.
AIIDE-2015-SifaHRDKB #game studies #mobile #predict: Predicting Purchase Decisions in Mobile Free-to-Play Games (RS, FH, JR, AD, KK, CB), pp. 79–85.
AIIDE-2016-DrachenLKRSRK #agile #game studies #mobile #predict: Rapid Prediction of Player Retention in Free-to-Play Mobile Games (AD, ETL, YK, PSR, RS, JR, DK), pp. 23–29.
CIG-2016-DrachenGGHLSK #analysis #behaviour #clustering #comparative #profiling: Guns and guardians: Comparative cluster analysis and behavioral profiling in destiny (AD, JG, CG, EH, PL, RS, DK), pp. 1–8.
CIG-2016-DrachenRRS #game studies #mobile: Stylized facts for mobile game analytics (AD, NR, JR, RS), pp. 1–8.
CIG-2016-SifaSDOB #game studies #learning #predict #representation: Predicting Retention in Sandbox Games with Tensor Factorization-based Representation Learning (RS, SS, AD, CO, CB), pp. 1–8.
CIG-2016-TamassiaRSDZH #approach #game studies #markov #modelling #online #predict: Predicting player churn in destiny: A Hidden Markov models approach to predicting player departure in a major online game (MT, WLR, RS, AD, FZ, MH), pp. 1–8.
AIIDE-2017-RavariSSD #game studies #hybrid #online #predict: Predicting Victory in a Hybrid Online Competitive Game: The Case of Destiny (YNR, PS, RS, AD), pp. 207–213.
CHI-PLAY-2017-PetersenTMD #approach #game studies #mobile: Evaluating the Onboarding Phase of Free-toPlay Mobile Games: A Mixed-Method Approach (FWP, LET, PMB, AD), pp. 377–388.
CHI-PLAY-2017-SchaekermannRWK #behaviour #game studies #metric #motivation #profiling #self: Curiously Motivated: Profiling Curiosity with Self-Reports and Behaviour Metrics in the Game “Destiny” (MS, GR, GW, SK, DMJ0, AD, RS, LEN), pp. 143–156.
AIIDE-2018-CanossaMTD #profiling #string: Like a DNA String: Sequence-Based Player Profiling in Tom Clancy's The Division (AC, SM, JT, AD), pp. 152–158.
CIG-2018-AungBDCKYW #dataset #learning #predict #scalability: Predicting Skill Learning in a Large, Longitudinal MOBA Dataset (MA, VB, AD, PIC, AVK, CY, ARW), pp. 1–7.
ICGJ-2018-PirkerLPD #aspect-oriented #development #game studies #process #social: Social Aspects of the Game Development Process in the Global Gam Jam (JP, IL, AP, AD), pp. 9–16.
AIIDE-2019-DemediukYDWB #analysis #identification: Role Identification for Accurate Analysis in Dota 2 (SD, PY, AD, JAW, FB), pp. 130–138.
CHI-PLAY-2019-ScholtenHDDWZ: Ethereum Crypto-Games: Mechanics, Prevalence, and Gambling Similarities (OJS, NGJH, SD, AD, JAW, DZ), pp. 379–389.
CoG-2019-BonomettiRHWD #estimation #interactive #modelling #probability: Modelling Early User-Game Interactions for Joint Estimation of Survival Time and Churn Probability (VB, CR, MH, ARW, AD), pp. 1–8.
CoG-2019-KatonaSHDBDW #learning #predict #using: Time to Die: Death Prediction in Dota 2 using Deep Learning (AK, RJS, VJH, SD, FB, AD, JAW), pp. 1–8.
CoG-2019-WallnerKD #game studies #online #profiling #twitter: Tweeting your Destiny: Profiling Users in the Twitter Landscape around an Online Game (GW, SK, AD), pp. 1–8.
FDG-2019-AungDSTANRKSD #game studies #profiling: The trails of Just Cause 2: spatio-temporal player profiling in open-world games (MA, SD, YS, YT, YA, SN, SR, DK, RS, AD), p. 11.