Play-Persona: Modeling Player Behaviour in Computer Games

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use of game metrics for gameplay analysis is a relatively recent innovation, it remains uncertain what the limits are for their application and which methods that are cross-game applicable. The use of gameplay metrics is in itself novel, being hitherto applied in about a dozen different publications, despite the widespread use of instrumentation data within related fields such as application software production and website design. Existing approaches towards gameplay metrics analysis generally focus on single variables and rarely focus on the spatial dimension of the game worlds that players navigate. In this paper, two case studies have been presented which showcases the usefulness of working with multiple variables and within spatial environments, providing an approach for analyzing gameplay and recreating the playing experience. The case studies are based on common features of shooter-type games, navigation and death, and are therefore cross- applicable across games of this genre. Furthermore, the approaches presented are relevant for other kinds of games and VEs where the player controls a single character/avatar. The case studies indicate the usefulness of gameplay metrics in being able to recreate the play experience, as a method for evaluating game design, e.g. figuring out where the challenge level is too high or low, where the player has problems navigating, and if the player does what is intended from in the design, or goes outside it. In games where multiple means of completing the game are possible, using different strategies, such as Deus Ex, the Tomb Raider-series and role-playing games such as Neverwinter Nights, this latter potential is especially promising. ACKNOWLEDGEMENTS The current work would not have been possible without the stellar work of the EIDOS Online Development Team, who created the metrics server and –system, and who maintain it to the joy of the authors and other data analysts. Also sincere thanks to the many other colleagues at IO Interactive, EIDOS, the IT University of Copenhagen and the Danish Design School, for assistance, support and interest. REFERENCES 1. Bateman, C., Boon, R.: 21st Century Game Design. Charles River Media (2005) 2. Blythe, M. A., Overbeeke, K., Monk, A. F. Wright, P.C.: Funology – from usability to enjoyment. Springer (2004) 180
3. Börner, K., Penumarthy, S.: Social Diffusion Patterns in Three-Dimensional Virtual Worlds. Information Visualization, 2 (2003), 182-198 4. Chittaro, L. & Ieronutti, L.: A visual tool for tracing users´ behavior in virtual environments. In Proc. Working Conference on Advanced Visual Interfaces (2004), 40-47 5. Chittaro L., Ranon R., Ieronutti L.: VU-Flow: A Visualization Tool for Analyzing Navigation in Virtual Environments. IEEE Transactions on Visualization and Computer Graphics, 12,6 (2006), 1475-1485 6. Davis, J.; Steury, K., Pagulayan, R.: A survey method for assessing perceptions of a game: The consumer playtest in game design. Game Studies: The International Journal of Computer Game Research, 5 (2005) 7. De Kort, Y., Ijsselssteijn, W: People, places and play: Player experience in a socio-spatial context. ACM Computers in Entertainment 6, 2, article no. 18 (2007) 8. DeRosa, P.: Tracking Player Feedback to Improve Game Design. Gamasutra, Aug. 7 (2007) 9. Desurvire, H., Caplan, M., Toth, J. A.: Using heuristics to evaluate the playability of games. In CHI '04 extended abstracts on Human factors in computing systems (2004) 10. Dumas, J. S. 2003. User-Based Evaluations. In: The HCI handbook. Lawrence Erlbaum Associates (2003), 1093-1117 11. Gilleade, K. M., Dix, A.: Using frustration in the design of adaptive videogames. In: Proceedings of the ACM SIGCHI International Conference on Advances in Computer Entertainment Technology, Singapore (2004), 228-232 12. Goetz, P.: Too Many Clicks! Unit-Based Interfaces Considered Harmful. Gamasutra, August 23 (2006) 13. Hilbert, D. M. and Redish, J. C.: A practical guide to usability testing. Intellect books (1999) 14. Hoobler, N., Humphreys, G., Agrawala, M.: Visualizing 15. Competitive Behaviors in Multi-User Virtual Environments. In Proc. of the IEEE Visualization Conference (2004), 163-170 16. Hurst, A., Hudson, S. E., Mankoff, J.: Dynamic detection of novice vs. skilled use without a task model. In Proceedings of CHI 2007, ACM press (2007), 271-280 17. Ijsselsteijn, W., de Kort, Y., Poels, K., Jugelionis, A., Bellotti, F.: Characterizing and Measuring User Experiences in Digital Games. In: Proceedings of ACE, Toronto, Ontario (2007) 18. Isbister, K., Schaffer, N.: Game Usability: Advancing the Player Experience. Morgan Kaufman (2008) 181
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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
Page 129 and 130: event sets, i.e. capturing metrical
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Page 133 and 134: metrics include tracking camera vie
Page 135 and 136: Figure 4: Example of interaction me
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Page 139 and 140: players can choose between differen
Page 141 and 142: Play modes: Running, climbing, fall
Page 143 and 144: according to their positioning on a
Page 145 and 146: 14. Medlock, M.C., Wixon, D.; Terra
Page 147 and 148: ARTICLE 6 - PLAY-PERSONAS: BEHAVIOU
Page 149 and 150: experiences are evaluated and remem
Page 151 and 152: B) It is possible to infer user nee
Page 153 and 154: personas are hypotheses that emerge
Page 155 and 156: Expert Rookie Grunt Athlete Chess-
Page 157 and 158: Procedural description: Shooting: -
Page 159 and 160: In the case example for Tomb Raider
Page 161 and 162: 2. Cooper, A.: The Inmates Are Runn
Page 163 and 164: ARTICLE 7 - TOWARDS GAMEPLAY ANALYS
Page 165 and 166: The requirement for improving user-
Page 167 and 168: The literature on game metrics is m
Page 169 and 170: ecause they all stem from in-house
Page 171 and 172: If a player kills a traitor they re
Page 173 and 174: Figure 3 (Top): diagram showing the
Page 175 and 176: titles where there is a trend towar
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Page 179: player spent at the location and wh
Page 183 and 184: 34. Thompson, C.: Halo 3: How Micro
Page 185 and 186: ARTICLE 8 - ANALYZING SPATIAL USER
Page 187 and 188: on basic statistical analysis metho
Page 189 and 190: visualization or for creating overv
Page 191 and 192: egarding actions undertaken in-game
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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
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Personality is described as recogni
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Inputs: Outputs: Primary attack (ra
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Due to the fact that each game metr
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12. Jennett, C., Cox, A.L., Cairns,
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ARTICLE 11 - GAME METRICS AND BIOME
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His research interests are biometri
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5. R. L. Hazlett. Measuring emotion
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ARTICLE 12 - ANALYZING USER BEHAVIO
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elated to player-game interaction a
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10. Kuniavsky, M.: Observing the Us
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ARTICLE 13 - PATTERNS OF PLAY: PLAY
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Play-personas are here introduced a
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It is only natural that game design
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4 Defining play-personas using game
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TRU is the eighth installment in th
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gameplay, descriptiveness of player
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In the case of TRU, the three param
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into which sections of the levels t
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Runners: This group completes the g
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17. Iser, W.: The Implied Reader. J
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