Play-Persona: Modeling Player Behaviour in Computer Games

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REFERENCES 1. K. Isbister and N. Schaffer, Game Usability: Advancing the Player Experience. Morgan Kaufman, 2008. 2. J. H. Kim, D. V. Gunn, E. Schuh, B. C. Phillips, R. J. Pagulayan, and D. Wixon, “Tracking real- time user experience (true): A comprehensive instrumentation solution for complex systems,” in Proceedings of CHI, Florence, Italy, 2008, pp. 443–451. 3. A. Tychsen and A. Canossa, “Defining personas in games using metrics,” in Proceedings of Future Play 2008. Toronto, Canada: ACM publishers, 2008, pp. 73–80. 4. R. J. Pagulayan, K. Keeker, D. Wixon, R. L. Romero, and T. Fuller, The HCI handbook. Lawrence Erlbaum Associates, 2003, ch. Usercentered design in games, pp. 883–906. 5. A. Ultsch, Kohonen Maps, 1999, ch. Data Mining and Knowledge Discovery with Emergent Self-Organizing Feature Maps for Multivariate Time Series, pp. 33–46. 6. T. Kohonen, Self-Organizing Maps. Springer, 2001. 7. C. Thurau, C. Bauckhage, and G. Sagerer, “Combining self organizing maps and multilayer perceptrons to learn bot-behaviour for a commercial game,” in GAME-ON, 2003, pp. 119–123. 8. R. Houlette, Player Modeling for Adaptive Games. AI Game Programming Wisdom II. Charles River Media, Inc, 2004, pp. 557–566. 9. D. Charles and M. Black, “Dynamic player modelling: A framework for player-centric digital games,” in Proceedings of the International Conference on Computer Games: Artificial Intelligence, Design and Education, 2004, pp. 29–35. 10. C. Thurau, C. Bauckhage, and G. Sagerer, “Learning human-like Movement Behavior for Computer Games,” in From Animals to Animats 8: Proceedings of the 8th International Conference on Simulation of Adaptive Behavior (SAB-04), S. Schaal, A. Ijspeert, A. Billard, S. Vijayakumar, J. Hallam, and J.-A. Meyer, Eds. Santa Monica, LA, CA: The MIT Press, July 2004, pp. 315–323. 11. C. Thurau, T. Paczian, and C. Bauckhage, “Is bayesian imitation learning the route to believable gamebots?” International Journal of Intelligent Systems Technologies and Applications, vol. 2, no. 2/3, pp. 284–295, 2007. 12. R. Thawonmas, M. Kurashige, K. Iizuka, and M. Kantardzic, “Clustering of Online Game Users Based on Their Trails Using Self-organizing Map,” in Proceedings of Entertainment Computing - ICEC 2006, 2006, pp. 366–369. 226
13. G. N. Yannakakis and M. Maragoudakis, “Player modeling impact on player’s entertainment in computer games,” in Proceedings of the 10th International Conference on User Modeling; Lecture Notes in Computer Science, vol. 3538. Edinburgh: Springer-Verlag, 24–30 July 2005, pp. 74–78. 14. D. Thue, V. Bulitko, M. Spetch, and E. Wasylishen, “Interactive storytelling: A player modelling approach,” in The Third Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), Stanford, CA, 2007, pp. 43–48. 15. C. White and D. Brogan, “The Self Organization of Context for Learning in Multiagent Games,” in Proceedings of the Second Artificial Intelligence and Digital Interactive Entertainment Conference (AIIDE). AAAI Press, 2006, pp. 92–97. 16. R. Rosenthal, “Covert communication in laboratories, classrooms, and the truly real world,” Current Directions in Psychological Science, vol. 12, no. 5, pp. 151–154, 2003. 17. R. M. Gray, “Vector quantization,” IEEE ASSP Magazine, pp. 4–29, 1984. 18. A. Ultsch and L. Herrmann, “Architecture of emergent self-organizing maps to reduce projection errors,” in Proceedings of ESANN, 2005, pp. 1–6. 19. A. Ultsch and F. Moerchen, “Esom-maps: tools for clustering, visualization, and classification with emergent som,” Dept. of Mathematics and Computer Science, University of Marburg, No. 46, Tech. Rep., 2005. 20. J. H. J. Ward, “Hierarchical grouping to optimize an objective function,” Journal of the American Statistical Association, vol. 58, pp. 236–244, 1963. 21. A. Ultsch, “Maps for the Visualization of high-dimensional Data Spaces,” in Proceedings of the Workshop on Self-Organizing Maps, 2003, pp. 225–230. 227
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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
Page 58 and 59:
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
Page 175 and 176: titles where there is a trend towar
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Page 179 and 180: player spent at the location and wh
Page 181 and 182: 3. Börner, K., Penumarthy, S.: Soc
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
Page 193 and 194: ased methods within the games area
Page 195 and 196: 2.4 The EIDOS Metrics Suite The dat
Page 197 and 198: is played in Third-Person perspecti
Page 199 and 200: one cause of death, and performing
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
Page 225: equests for specific in-game puzzle
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 and 242: His research interests are biometri
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
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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