Play-Persona: Modeling Player Behaviour in Computer Games

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of user behavior via gameplay metrics act as a supplement to the established methods for user- oriented research in the game industry and –research. For example, usability testing focuses on measuring the ease of operation of a game, while playability testing explores is users have a good playing experience [24]. Gameplay metrics analysis offers however insights into how the users are actually playing the games being tested. This makes this type of data uniquely suited to form the basis for gameplay evaluations. In this paper, two case studies are presented that showcase novel forms of gameplay analyses that can be performed via the application of gameplay metrics. The case studies showcase new ways to analyze gameplay metrics and take advantage of the spatial dimension of certain metrics in order to target gameplay analysis. They are also indicative of the ability of gameplay metrics analysis to provide detailed data on player behavior, thereby providing a tool for not only game development and –design; but also general user-oriented research in interactive entertainment. While the case studies presented are based on two specific commercial titles, Kane & Lynch and Fragile Alliance (both: 2008, IO Interactive), the two games are examples of the highly popular shooter genre, and represent the two dominating modes of play within this genre: Single-player (Kane & Lynch) and multi-player (Fragile Alliance). The methods described in the case studies are therefore cross-game applicable, directly transferable to other shooter games (e.g. Doom 3, Unreal Tournament, Bioshock and Crysis), as well as other game genres utilizing a central character as the vessel for game-player interaction, i.e. also many role-playing games and adventure games such as Oblivion, Neverwinter Nights and Dreamfall. Additionally, massively-multiplayer online games such as Age of Conan and World of Warcraft and online persistent worlds such as Second Life, as well as other Virtual Environments (VEs), have players taking control of a single 3D character, and therefore also form potential targets for the kinds of analysis presented. The work presented here is being carried out in collaboration between Danish game developer IO Interactive (a subsidiary of EIDOS Entertainment), and the IT University of Copenhagen. 2. PREVIOUS WORK The focus of this paper is not to review existing work on gameplay metrics but rather to showcase a series of developed analysis methods for evaluating gameplay. However, a brief overview of the related work is presented here. 166
The literature on game metrics is minimal, even including the information available from industry sources such as the Game Developers Conference. The handful of published examples of gameplay metrics analysis is restrictive in the amount of information that they provide [e.g. 5,19,17,25,35]. This is in part due to the novelty of the approach, in part because gameplay metrics data would normally be treated as confidential by a company. Furthermore, the few examples of systems developed for capturing game metrics-like data developed outside of the academia are generally targeted at Virtual Environments (VEs) rather than games specifically [e.g. 3,4,5,14]. Additionally, these applications are often targeted at analyzing specific features, such as movement only, or developed for use with specific applications/games [e.g. 14], and therefore not portable across environments. A substantial part of the current published material about gameplay metrics analysis stems from the work of Microsoft Game Labs, which perform game testing and user-oriented research for the various Microsoft-based game studios. Microsoft Game Labs developed e.g. the TRUE setup to capture gameplay metrics together with survey and video data. The approach has been applied to user testing of e.g. Halo 2 and Shadowrun [19,33]. Among the most recent uses of game metrics in a research context is the work by Williams et al. [36], who examined user data from EverQuest 2 in combination with survey-based information to evaluate player behavior, e.g. in terms of how many hours they spend gaming per week. Other examples include the data presented by Mellon [25] from the massively multiplayer online game (MMOG) The Sims Online; while Goetz [12] presented analysis of interface-based metrics from e.g. Civilization IV. Finally, DeRosa [8] reported from the use of time-spent reports developed at Bioware for their international hit game Mass Effect. Tychsen & Canossa [35] described a model for using gameplay metrics to describe personas of players in game design & -testing. What is apparent from the literature is that there is a need to open up the discussion about how to utilize gameplay metrics analysis in game production and – research, as the relatively limited size of the literature on the topic betrays the increasing amount of attention and use that the automated collection of gameplay metrics has gained within the past five years or so [32]. Notably within massively multi-player online games such as World of Warcraft and Age of Conan is the tracking of at least some basic gameplay metrics today a common practice during the development and live periods of the lifetime of these games [see e.g. 25]. 167
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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
Page 115 and 116: 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: The requirement for improving user-
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 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
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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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