Play-Persona: Modeling Player Behaviour in Computer Games

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1. INTRODUCTION In the past, user-centered game testing [16, 22] has largely borrowed methods from the human- computer interaction [17, 3] and the user experience community [6, 18, 19], which uses principally qualitative measures, like think-aloud protocol, interviews and surveys or a combination of them [8]. While these methods provide great insights into individual user experiences and allow developers to get an estimation of how their game is perceived by their players, they lack the empirical power of quantification. Previous panels and workshops explored for example differences between user research methods for games and productivity software [9], untapped video game genres [7], user experience measurement in games [1] and evaluation of the player experience in games [2]. This panel will take this discussion one step further by looking at the status quo of game metrics evaluation and the future of player evaluation. It will consider how new types of data, including psychophysiological instrumentation (e.g electroencephalography (EEG), electromyography (EMG), and eye tracking) or problem reports can be incorporated into the existing game evaluation process. Recently, there has been a change in many game companies toward using quantitative data in game evaluation [21]. This move comes together with research efforts into biometric evaluation of play [4, 5, 10, 11, 12, 13, 14, 15]. There is, however, a need to discuss the benefits and limitations of these evaluative methods and to compare them to classic qualitative evaluation methods. Tychsen and Canossa [21, 20] provided a distinction of game metrics in (1) navigation metrics, (2) interaction metrics, (3) narrative metrics, and (4) interface metrics. We will extend this definition to include biometric player data and seek to explore and establish a more precise taxonomy, usable application methods, and research and industry practices. The panel has the goal to provide a platform for industry and academia to discuss the current challenges in game metrics and biometric research and practice. This can only be done by comparing benefits and limitations of qualitative and quantitative methods. 2. PRESENTERS 2.1 Lennart Nacke Lennart Nacke has a computer engineer's degree (Dipl.Ing.) in Computational Visualistics from Otto-von-Guericke University, and is currently a Ph.D. candidate in Digital Game Development at the Blekinge Institute of Technology. 240
His research interests are biometric/psychophysiological evaluation of game experience, playability metrics and innovative interaction design for digital games. He moderates the panel. 2.2 Mike Ambinder Dr. Mike Ambinder has a Ph.D. in Experimental Psychology (with a focus on visual cognition) from the University of Illinois and a B.A. in Computer Science and Psychology from Yale University. At Valve, he works on the application of knowledge and methodologies from psychology to game design. 2.3 Alessandro Canossa Alessandro Canossa has a M.A. in Science of Communication, from the University of Turin. During the late nineties he was Art Director in advertisement and found his vocation/obsession in game development (LEGO, Tabula Rasa Games). Since 2006 he has worked at IO Interactive on his Ph.D. 2.4 Anders Drachen Dr. Anders Drachen is a post doctoral research fellow at the IT University of Copenhagen. His research focuses on user experience and games-based user-oriented research using gameplay metrics. 2.5 Regan Mandryk Dr. Regan Mandryk is an Assistant Professor in Computer Science at the University of Saskatchewan. Among her degrees are a M.Sc. in Kinesiology (2000), and a Ph.D. in Computing Science, both from Simon Fraser University (2005). Her current research interests are on sensing and modeling user state, designing interaction techniques and games for emerging devices and to encourage healthy living. 2.6 David Pinelle Dr. David Pinelle has a Ph.D. in Computer Science from the University of Saskatchewan and a B.Sc. in Occupational Therapy from Texas Tech University. He is working as a Research Officer with the Institute of Information Technology at the National Research Council, where he focuses on understanding and classifying game usability problems. 241
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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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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
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Page 215 and 216: (ESOM) [5] to emphasize the distinc
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Page 219 and 220: weight values we repeat the trainin
Page 221 and 222: TABLE I THE FOUR PLAYING BEHAVIOR C
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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: ARTICLE 11 - GAME METRICS AND BIOME
Page 243 and 244: 5. R. L. Hazlett. Measuring emotion
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
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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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