Play-Persona: Modeling Player Behaviour in Computer Games

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3. POINTS OF INTERACTIVITY Each panelist will introduce themselves in a fast-paced, vibrant, 30-seconds ash entrance. The panel moderator will subsequently put controversial statements about game metrics up for discussion by the panelists. A main point of discussion will be the integration of qualitative and quantitative game evaluation methods. This part of the panel is a moderated, interactive discussion between experts from academia and industry about current issues in metrical game evaluation. The discussion will be guided to provoke thoughts about best practices in research and industry for using game metrics. The last 10 minutes of the panel are reserved for questions from the audience. 4. TAKE-HOME MESSAGE Participants will have a clear understanding of the current state of the art in biometrics and game metrics methods as well as suggestion on how to incorporate these into existing processes for evaluating player experience. 5. REFERENCES 1. R. Bernhaupt, M. Eckschlager, and M. Tscheligi. Methods for evaluating games: how to measure usability and user experience in games? In Proceedings of the international conference on Advances in computer entertainment technology, pages 309{310, Salzburg, Austria, 2007. ACM. DOI = 10.1145/1255047.1255142. 2. R. Bernhaupt, W. Ijsselsteijn, F. F. Mueller, M. Tscheligi, and D. Wixon. Evaluating user experiences in games. In Proceedings of CHI '08 extended abstracts on Human factors in computing systems, pages 3905{3908, Florence, Italy, 2008. ACM. DOI = 10.1145/1358628.1358953. 3. C. Fabricatore, M. Nussbaum, and R. Rosas. Playability in video games: a qualitative design model. Human-Computer Interaction, 17(4):311{368, 2002. DOI = 10.1207/S15327051HCI1704 1. 4. M. Grimshaw, C. A. Lindley, and L. Nacke. Sound and immersion in the _rst-person shooter: Mixed measurement of the player's sonic experience. In Proceedings of the AudioMostly 2008, 3rd Conference on Interaction with Sound, Pitea, Sweden, 2008. Audio Mostly. retrieved from http://gamescience.bth.se. 242
5. R. L. Hazlett. Measuring emotional valence during interactive experiences: boys at video game play. In CHI '06: Proceedings of the SIGCHI conference on Human Factors in computing systems, pages 1023{1026, Montr_eal, Qu_ebec, Canada, 2006. ACM Press. DOI = 10.1145/1124772.1124925. 6. A. H. J_rgensen. Marrying hci/usability and computer games: a preliminary look. In Proceedings of the third Nordic conference on Human-computer interaction, pages 393{396, Tampere, Finland, 2004. ACM. DOI = 10.1145/1028014.1028078. 7. K. Keeker, R. Pagulayan, J. Sykes, and N. Lazzaro. The untapped world of video games. In Proceedings of CHI '04 extended abstracts on Human factors in computing systems, pages 1610{1611, Vienna, Austria, 2004. ACM Press. DOI = 10.1145/985921.986170. 8. J. H. Kim, D. V. Gunn, E. Schuh, B. Phillips, R. J. Pagulayan, and D. Wixon. Tracking real-time user experience (true): a comprehensive instrumentation solution for complex systems. In Proceeding of the twenty-sixth annual SIGCHI conference on Human factors in computing systems, pages 443{452, Florence, Italy, 2008. ACM. DOI = 10.1145/1357054.1357126. 9. N. Lazzaro and K. Keeker. What's my method?: a game show on games. In Proceedings of CHI '04: CHI '04 extended abstracts on Human factors in computing systems, pages 1093{1094. ACM, 2004. DOI = 10.1145/985921.985922. 10. R. Mandryk. Physiological measures for game evaluation. In K. Isbister and N. Scha_er, editors, Game Usability: Advice from the Experts for Advancing the Player Experience, pages 207{235. Elsevier Science and Technology Books, Burlington, MA, USA, 2008. 11. R. Mandryk and M. Atkins. A fuzzy physiological approach for continuously modeling emotion during interaction with play environments. International Journal of Human-Computer Studies, 6(4):329{347, 2007. DOI = 10.1016/j.ijhcs.2006.11.011. 12. R. Mandryk, M. Atkins, and K. Inkpen. A continuous and objective evaluation of emotional experience with interactive play environments. In Conference on Human Factors in Computing Systems (CHI 2006), pages 1027{1036, Montreal, Canada, 2006. ACM Press. DOI = 10.1145/1124772.1124926. 13. R. Mandryk and K. Inkpen. Physiological indicators for the evaluation of co-located collaborative play. In Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW 2004), pages 102{111, Chicago, USA, 2004. ACM Press. DOI = 10.1145/1031607.1031625. 243
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Play-Persona: Modeling Player Behav
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4.2.3 PLAYER EXPRESSION 53 4.3 PEIR
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ACKNOWLEDGEMENTS I would like to th
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ABSTRACT This thesis proposes a fra
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Analysis seeks to break down comple
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Considering games as acts of commun
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ensemble - the syntagm. Selection r
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described in terms of likelihood of
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ook: FLUX, published in 2008. This
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2. A THEORETICAL FRAMEWORK FOR MODE
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“positivism” and “anti-positi
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with the game world based solely on
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on that text. Later, Barthes introd
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according to their mental state. Wh
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Following the humanistic tradition
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In most games it is not realistic t
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ehaviour. Assessing assumptions on
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Independently on the identity of ea
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main focus of the brand has always
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3.4 Play-persona as a lens (a poste
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Figure 10: personality and subperso
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4. CREATING EXPERIENCES The MDA fra
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Play-styles are sets of isotopic ga
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4.2.3 Player expression Player expr
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Players will eventually understand
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Combining both of these two approac
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REFERENCES 1. Aarseth, E. (1997): C
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52. Hunicke, R. LeBlanc, M. & Zubek
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96. Wiggins, A. (2006): Data Driven
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ARTICLE 1 - TOWARDS A THEORY OF THE
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flow between the player, the avatar
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6. The player constructs knowledge
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are sets of rational, bodily, and b
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ehaviorism asserting that a compreh
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4.3 Origin of subpersonalities Acco
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environment and the emanating subpe
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flow line), one must increase the c
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7.1 Explorer’s path FIGURE 5 Expl
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the roof section, where he/she will
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14. DE SOUSA, R. 2003. Emotions. In
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ARTICLE 2 - DESIGNING LEVELS FOR EN
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2 STRUCTURE OF A GAME LEVEL The fir
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3.2 Symbolical Reading The movement
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The hangar is open for workers and
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gather info, spying through keyhole
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6.2 Play-Personas I am going to def
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The Butcher persona would have quit
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ARTICLE 3 - WAVING EXPERIENCES: PLA
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types" [12]. Personas are construct
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esults in the relatively easy task
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Crew actions schemas are: • Squad
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choice given until the mark “poli
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REFERENCES 1. Bozec, P. "My Everyth
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ARTICLE 4 - WEAVING EXPERIENCES IN
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In order to be able to take acti
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persona could be narratively define
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of non lethal, silent, and clean de
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ARTICLE 5 - DEFINING PERSONAS IN GA
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software is however limited in that
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event sets, i.e. capturing metrical
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objects and entities within it. Eve
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metrics include tracking camera vie
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Figure 4: Example of interaction me
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looting is a play-style observed in
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players can choose between differen
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Play modes: Running, climbing, fall
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according to their positioning on a
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14. Medlock, M.C., Wixon, D.; Terra
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ARTICLE 6 - PLAY-PERSONAS: BEHAVIOU
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experiences are evaluated and remem
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B) It is possible to infer user nee
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personas are hypotheses that emerge
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Expert Rookie Grunt Athlete Chess-
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Procedural description: Shooting: -
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In the case example for Tomb Raider
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2. Cooper, A.: The Inmates Are Runn
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ARTICLE 7 - TOWARDS GAMEPLAY ANALYS
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The requirement for improving user-
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The literature on game metrics is m
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ecause they all stem from in-house
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If a player kills a traitor they re
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Figure 3 (Top): diagram showing the
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titles where there is a trend towar
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a secondary variable together with
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player spent at the location and wh
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3. Börner, K., Penumarthy, S.: Soc
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34. Thompson, C.: Halo 3: How Micro
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ARTICLE 8 - ANALYZING SPATIAL USER
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on basic statistical analysis metho
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visualization or for creating overv
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Page 195 and 196: 2.4 The EIDOS Metrics Suite The dat
Page 197 and 198: is played in Third-Person perspecti
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Page 201 and 202: ArcGIS permits different layers to
Page 203 and 204: 4. Cadez, I., Heckerman, D., Meek,
Page 205 and 206: 35. Whiteside, J., Archer, N. P., W
Page 207 and 208: ARTICLE 9 - PLAYER MODELING USING S
Page 209 and 210: unsupervised learning in capturing
Page 211 and 212: 3. TOMB RAIDER: UNDERWORLD The popu
Page 213 and 214: 2 - Environment; the percent of tot
Page 215 and 216: (ESOM) [5] to emphasize the distinc
Page 217 and 218: populated middle-left cluster (Fig.
Page 219 and 220: weight values we repeat the trainin
Page 221 and 222: TABLE I THE FOUR PLAYING BEHAVIOR C
Page 223 and 224: (d) Number of Deaths, D (e) Complet
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Page 227 and 228: 13. G. N. Yannakakis and M. Maragou
Page 229 and 230: ARTICLE 10 - PSYCHOLOGY OF PERSONAL
Page 231 and 232: Personality is described as recogni
Page 233 and 234: Inputs: Outputs: Primary attack (ra
Page 235 and 236: Due to the fact that each game metr
Page 237 and 238: 12. Jennett, C., Cox, A.L., Cairns,
Page 239 and 240: ARTICLE 11 - GAME METRICS AND BIOME
Page 241: His research interests are biometri
Page 245 and 246: ARTICLE 12 - ANALYZING USER BEHAVIO
Page 247 and 248: elated to player-game interaction a
Page 249 and 250: 10. Kuniavsky, M.: Observing the Us
Page 251 and 252: ARTICLE 13 - PATTERNS OF PLAY: PLAY
Page 253 and 254: Play-personas are here introduced a
Page 255 and 256: It is only natural that game design
Page 257 and 258: 4 Defining play-personas using game
Page 259 and 260: TRU is the eighth installment in th
Page 261 and 262: gameplay, descriptiveness of player
Page 263 and 264: In the case of TRU, the three param
Page 265 and 266: into which sections of the levels t
Page 267 and 268: Runners: This group completes the g
Page 269 and 270: 17. Iser, W.: The Implied Reader. J
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Play-Persona: Modeling Player Behaviour in Computer Games

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