Play-Persona: Modeling Player Behaviour in Computer Games

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which can subsequently be analyzed to evaluate whether the playtester is moving through the map as intended and if any problems are encountered and where [see also 32]. In this example, the map is treated as a traditional paper map, i.e. it is not interactive. In a GIS, it is possible to tag map features with detailed information in the form of attributes: For example, the level map could be divided into sub-sections and each section be named individually and contain various attributes such as degree of lighting, number of enemies, expected completion time etc. When mapping gameplay metrics on top of maps that themselves contain detailed feature information, a high degree of flexibility in the gameplay metrics analysis is gained, for example in calculating the number of kills occurring in “inside” type environments vs. “outside” type environments in an FPS – as well as specific numbers for each map feature. Conceptually, a GIS is reminiscent of the architecture of computer game systems that feature virtual environments linked with objects and entities whose attributes are stored in databases [9,22]. The difference is that a GIS is specifically engineered towards analysis of the featured data and their underlying attributes in a spatial environment (although analysis can also happen outside of the mapping environment, for example in building models or querying the underlying attribute databases). In practice, gameplay metrics are added in the form of layers on top of a level or zone map (Figure 1). GIS allows the placement of several layers and performing calculations along and across the associated multiple variables and their attributes. Figure 1: A GIS represent different data sets as layers on top of a game level map 194
2.4 The EIDOS Metrics Suite The data for the case studies presented here are derived from the game Tomb Raider: Underworld (Crystal Dynamics, 2008). The data have been logged via the EIDOS Metrics Suite (Figure 2), an instrumentation system is designed to be able to interact with the existing user-oriented research and testing at the EIDOS studios and development houses; during production as well as in the live period, in addition to providing gameplay metrics data. It is constructed so as to facilitate the collection of data from any EIDOS-produced game, and to deliver these data directly to a variety of analysis software, e.g. the native GIS. The user research conducted at within EIDOS, e.g. at IO Interactive usually involves an array of methods, recordings and analyses [e.g. 16,25,33]. These varied data input are generally utilized together (e.g. attitudinal data, screen capture and gameplay metrics), as the various user research testing methods come with inherent strengths and weaknesses, requiring method triangulation to produce viable results. The EIDOS metrics suite has several features in common with instrumentation techniques used in the general HCI-field, e.g. that users interact with a system that automatically record application events of interest in message streams, which are send to a central server for analysis. The metrics suite was developed by and is maintained by the EIDOS Online Development Team, located at IO Interactive in Denmark. The team is responsible for maintaining the metrics suite and assist EIDOS developers in enabling and tracking metrics. Figure 2: General framework for the gameplay metrics capture and analysis process at IO Interactive. Data are captured from testers (players) to a central EIDOS SQL server, from where they are drawn into different software analysis packages such as SPSS or ArcGIS for cleaning, evaluation and analysis. The data are then exported to visualization software), and a report developed for the target user (e.g. designer, QA). 195
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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
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 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: ased methods within the games area
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 and 226: equests for specific in-game puzzle
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 and 242: His research interests are biometri
Page 243 and 244: 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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