Play-Persona: Modeling Player Behaviour in Computer Games

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In terms of using gameplay metrics as a user-oriented testing method, this is not without specific challenges. Where methods such as usability-testing and playability-testing focus on establishing whether a player can interact with a game effectively, and if doing so is fun, gameplay metrics- analysis is targeted at clarifying what the player is actually doing, and sometimes why the behavior indicated arises. Importantly, the “why” is hypothesis-based: In order to assess the motivations and reasons behind player behaviors, it is often necessary to combine gameplay metrics-analysis with other user-oriented testing methods. Furthermore, gameplay metrics does not inform about the gender, age or other demographics of the player. As such, the approach supplements existing methods, addressing the key weakness of standard user-oriented game testing methods, namely the lack of detailed and objective data. Finally, it should be noted that order to enable metrics- based analysis, an infrastructure is needed to capture the data, which includes substantial storage needs in the case of large commercial titles such as Kane & Lynch. In summary, the literature on gameplay metrics and game metrics analysis is largely based on data from in-house testing, with a few MMOG-related examples of data derived from installed clients or game servers such as Williams et al. [36]. Analyses have, with the exception of the work by Microsoft Game Labs, focused on high-level aggregate-counts of metrics. The spatial component of gameplay metrics have not been the focus of published work outside of the heatmaps (game level maps showing the locations of player character death aggregated for a number of players), which have been published for games such as Half Life 2 and Team Fortress 2; and visualizations of player progression as a function of time for Halo 3 [33]. Previous work with metrics has generally included only a single variable at a time, e.g. level completion times, and the potential for performing analysis on the spatial dimension of gameplay metrics (e.g. the position of the player within the virtual environment) limited to handful of industry- and academia-derived applications. There is therefore a substantial room for development of novel approaches towards utilizing gameplay metrics, and this forms the overarching goal of the research presented here. 3. CASE STUDIES The data used as a basis for the case studies presented here is test data, i.e. data from in-house testing with professional testers or representatives of the target audience for the games in question. A varied amount of game sessions/number of people are used in the datasets for the different case studies, however, it should be noted that essentially the data suffer from a bias 168
ecause they all stem from in-house testing at IO Interactive, not testing carried out in an independent laboratory. However, the purpose of the case studies is not to present conclusions from experiments performed, but to showcase techniques and methodologies, which can be employed in both an industry and research-oriented context. It should also be noted that the data are derived from tests run on unfinished versions of the games involved, and the data therefore do not represent material from the finished games. This is in the current context not a problem in terms of the quality of the analyses presented, rather it adds strength to the argument of e.g. [19,35] that gameplay metrics form a valuable supplement to other user-oriented research methods during the production phases of computer game development (where testing is carried out iteratively with more and more complete versions of the game/game levels). Due to the confidential nature of the gameplay metrics data, no absolute numbers are presented. Data for the case study is drawn from the EIDOS metrics suite, developed by the EIDOS Online Development Team at IO Interactive in Denmark. The metrics suite is an instrumentation solution engineered to provide EIDOS studios with metrics data for e.g. user behavior analysis. Logged information is streamed to a central server for extraction and analysis. The metrics suite can collect data from game engine software during production as well as from installed clients during the live period of the lifetime of a game (i.e. following launch and distribution to the customers) [19,35]. In addition to utilizing gameplay metrics in user-oriented testing, IO Interactive and other EIDOS developers potentially involves a battery of methods including audiovisual recording and analysis, survey-based approaches, expert testing, different forms of usability testing, biofeedback, eye tracking etc. [27]. The combination of gameplay metrics analysis with existing methods for user- oriented game testing permits strengthens across-method triangulation of results. Following transmission from the game engine, the data is transformed through and ETL process (Extract, Transform and Load) and imported to an SQL server. From the server, analysts extract data for analysis and visualization, creating reports for the interested parties (generally QA, designers, producers and marketing). The transformation of raw gameplay metrics data drawn from the EIDOS metrics suite, into reports disseminated to the interested stakeholders requires a series of potentially iterative steps. When performing analyses of gameplay metrics with a spatial component, data are imported into a geodatabase system from where files are drawn, analyzed and visualized using a Geographical Information System [22], ArcGIS. Some of the gameplay metrics can be quickly visualized using a 169
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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
Page 117 and 118: ARTICLE 4 - WEAVING EXPERIENCES IN
Page 119 and 120: In order to be able to take acti
Page 121 and 122: persona could be narratively define
Page 123 and 124: of non lethal, silent, and clean de
Page 125 and 126: ARTICLE 5 - DEFINING PERSONAS IN GA
Page 127 and 128: software is however limited in that
Page 129 and 130: event sets, i.e. capturing metrical
Page 131 and 132: objects and entities within it. Eve
Page 133 and 134: metrics include tracking camera vie
Page 135 and 136: Figure 4: Example of interaction me
Page 137 and 138: looting is a play-style observed in
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: The literature on game metrics is m
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 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.
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weight values we repeat the trainin
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TABLE I THE FOUR PLAYING BEHAVIOR C
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(d) Number of Deaths, D (e) Complet
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equests for specific in-game puzzle
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13. G. N. Yannakakis and M. Maragou
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ARTICLE 10 - PSYCHOLOGY OF PERSONAL
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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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