Play-Persona: Modeling Player Behaviour in Computer Games

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Designers provide players with a variety of actions (ludic affordances) to perform in the game space (aesthetic affordances), in return players select among the array of possibilities to express their behavior. Disregarding the second layer of the communication channel would inevitably lead to solipsistic design. 4.1 Steps of the transaction between designers and players Analyzing in further detail the negotiation between designers and players (figure 12) and breaking down the previous diagram (figure 11), it is possible to identify certain steps. Figure 12: steps of the transaction between designer and player The conceptual scaffolding that allows designers to facilitate the emergence of personalized experience within the framework of a game unfolds through several steps. Play values are the abstract principles, both aesthetic and functional, that the game’s design should embody [102], not unlike the aesthetics from the MDA framework. Lindley [65, 103] defines gameplay gestalts as patterns of interaction between player and game and the cognitive structures that underlie and facilitate this interaction. A list of all the gameplay gestalts in a game would cover all possible navigation and interaction attitudes that players can utilize to negotiate any given situation. Gestalts are the features that motivate, facilitate and constrain player action, offering them methods of operation within the game. They roughly correspond to MDA’s mechanics. 50
Play-styles are sets of isotopic gameplay gestalts which signify, unify or distinguish players from each other. They represent possible ways in which certain subsets of the rules and mechanics provided by the game can be combined. For example a “rush attack” in any real-time strategy game consists of quickly building a large number of the weakest and cheapest units and flooding the opponent with wave after wave of them. Play-styles correspond to MDA’s dynamics, how mechanics are used during a game session. Play-personas are an even bigger and more abstract construct; they allow designers to prefigure possible patterns of behaviour and at the same time allow players to unify their actions and make sense of their behaviour. Play-personas emerge from the various options for actions that players are given. Play-personas provide ways to personalize the playing experience and facilitate players’ expression. The second layer of the communication channel that goes from players to designers should be deconstructed both with quantitative and qualitative tools. Game metrics, being techniques for inferring higher-order concepts from the large quantity of information available when a computer game is played, provide detailed and granular records of players’ actions. This allows designers to reconstruct players’ in-game behaviour. Of course game metrics can only account for what a player does, where and when, but their merit is also the fact that this information can be harvested remotely even after the game has been released on the market. In order to discover more about players (who), their motivations (why) and the context of play (how), it is necessary to resort to surveys and interviews. 4.2 Unfolding the dialectic “designer-player” The model utilized insofar clarifies the relation that games build between designers and players (figure 12). Designers provide aesthetic and ludic affordances and they both contribute in shaping and allowing for player expression (figure 13). 4.2.1 Aesthetic affordances Aesthetic affordances consist of those possibilities for action that emerge from the sensory- perceptual qualities of the world such as navigation patterns and viewing ranges; they describe where the avatar can go and see. The aesthetical elements comprise for example colors, textures, shapes, lights, ambient sounds and music; pretty much anything that invests the senses immediately, purely in the realm of perception as un-mediated, mentally unprocessed, event. Perception is here intended as one of the two meanings of the term experience: a particular 51
Page 1: Play-Persona: Modeling Player Behav
Page 4 and 5: 4.2.3 PLAYER EXPRESSION 53 4.3 PEIR
Page 7 and 8: ACKNOWLEDGEMENTS I would like to th
Page 9: ABSTRACT This thesis proposes a fra
Page 12 and 13: Analysis seeks to break down comple
Page 14 and 15: Considering games as acts of commun
Page 16 and 17: ensemble - the syntagm. Selection r
Page 18 and 19: described in terms of likelihood of
Page 20 and 21: ook: FLUX, published in 2008. This
Page 23 and 24: 2. A THEORETICAL FRAMEWORK FOR MODE
Page 25 and 26: “positivism” and “anti-positi
Page 27 and 28: with the game world based solely on
Page 29 and 30: on that text. Later, Barthes introd
Page 31 and 32: according to their mental state. Wh
Page 33: Following the humanistic tradition
Page 36 and 37: In most games it is not realistic t
Page 38 and 39: ehaviour. Assessing assumptions on
Page 40 and 41: Independently on the identity of ea
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Page 44 and 45: 3.4 Play-persona as a lens (a poste
Page 46 and 47: Figure 10: personality and subperso
Page 49: 4. CREATING EXPERIENCES The MDA fra
Page 53 and 54: 4.2.3 Player expression Player expr
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Page 58 and 59: Combining both of these two approac
Page 61 and 62: REFERENCES 1. Aarseth, E. (1997): C
Page 63 and 64: 52. Hunicke, R. LeBlanc, M. & Zubek
Page 65: 96. Wiggins, A. (2006): Data Driven
Page 69 and 70: ARTICLE 1 - TOWARDS A THEORY OF THE
Page 71 and 72: flow between the player, the avatar
Page 73 and 74: 6. The player constructs knowledge
Page 75 and 76: are sets of rational, bodily, and b
Page 77 and 78: ehaviorism asserting that a compreh
Page 79 and 80: 4.3 Origin of subpersonalities Acco
Page 81 and 82: environment and the emanating subpe
Page 83 and 84: flow line), one must increase the c
Page 85 and 86: 7.1 Explorer’s path FIGURE 5 Expl
Page 87 and 88: the roof section, where he/she will
Page 89 and 90: 14. DE SOUSA, R. 2003. Emotions. In
Page 91 and 92: ARTICLE 2 - DESIGNING LEVELS FOR EN
Page 93 and 94: 2 STRUCTURE OF A GAME LEVEL The fir
Page 95 and 96: 3.2 Symbolical Reading The movement
Page 97 and 98: The hangar is open for workers and
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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
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titles where there is a trend towar
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a secondary variable together with
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player spent at the location and wh
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3. Börner, K., Penumarthy, S.: Soc
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34. Thompson, C.: Halo 3: How Micro
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ARTICLE 8 - ANALYZING SPATIAL USER
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on basic statistical analysis metho
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visualization or for creating overv
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egarding actions undertaken in-game
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ased methods within the games area
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2.4 The EIDOS Metrics Suite The dat
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is played in Third-Person perspecti
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one cause of death, and performing
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ArcGIS permits different layers to
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4. Cadez, I., Heckerman, D., Meek,
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35. Whiteside, J., Archer, N. P., W
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ARTICLE 9 - PLAYER MODELING USING S
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unsupervised learning in capturing
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3. TOMB RAIDER: UNDERWORLD The popu
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2 - Environment; the percent of tot
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(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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