Play-Persona: Modeling Player Behaviour in Computer Games

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6 PHENOMENOLOGICAL ANALYSIS 6.1 Play-Modes Looking at the data harvested during the aesthetical and ludic analysis allows us to make assumptions about “play-modes”. Play-modes are preferential attitudes towards navigation of the world and interaction with characters. There are four navigation attitudes emerging: - BRAWN, physical prowess, climbing, making use of force, always moving - BRAIN, observation skills, finding holes in the surveillance system, patterning behaviours, waiting - ROLE PLAYING, dressing up like other characters and acting the part - HIDING/SNEAKING, keeping the suit, walking in shadows (and closets) The interaction attitudes are structured according to four binary variables (see figure 9): - Lethal / non-lethal - Silent / loud - Clean / bloody - Single / multiple effect Fig. 9 Play-modes Each one of these playing modes triggers some basic emotional response in the player. Emot-emes, or basic emotions, are emotions that are impossible to deconstruct further. When a player behaves consistently in the game he/she uses only play-modes that share a similar pattern of emot-emes: the player identifies completely with a certain profile. 100
6.2 Play-Personas I am going to define these consistent profiles “play-personas”. In spite of the fact that players tend to mix play-modes, it is clear that the designers themselves acknowledge the possibility to play out a persona since they reward the “Silent Assassin”. Playing as the Silent Assassin means only killing the target and getting out with the suit. All targets should preferably die by "accident“. Beside the Silent Assassin, looking at the play-modes shows other possible play-personas emerging: THE BUTCHER: up, close and personal. Did I mention he likes it bloody? THE SNIPER: this persona loves to camp and take out the victim from far away. DIRTY HARRY: a gun-toting extrovert that relates mostly to lead-ridden bodies. THE PARTY ANIMAL: also known as “the transvestite”, he likes nothing more than changing outfits. THE SHADOW: sticking to stealth movements. UNABOMBER: not satisfied unless it involves explosions. IN-AND-OUT: rushing through as fast as possible, the keyword is speed. SAM FISHER: the main priority is to show off cool animations as much as possible. THE AESTHETE: inconsistently looking for “interesting” scenarios. The game designers did not plan the level for them. As I found out comparing internal design documents and walkthroughs on the net, they might not even have been aware of all the play- modes. These personas emerged from the tools that the players were given. Play-personas provide ways to customize the playing experience and allow players to express themselves with no additional cost, requiring no redundancy of assets or expensive adaptive technology. 7 FROM ANALYSIS TO DESIGN The analytical work operated until now on an existing game level has definitely helped recognizing players’ behavioural patterns and has its uses as a tool for evaluating the success of a level by comparing goals set by the designers with the actual experiences that players are having. But the real impact of this method can be fully appreciated if it is applied from the beginning in order to proficiently plan, orchestrate and map out player experiences in the level. Now how can we use the mental tools defined so far to actually help in the design phase of making levels instead of just an evaluation tool? EmSense, a San Francisco-based company, was hired by Epic Games to help fine- tuning the level design for the game “Gears of War”. What they did was to turn Csikszentmihalyi’s Flow diagram 45 degrees and chart on it the biological feedback of people playing the game. They asked players to wear a head band that can record EEG, skin conductivity response, heart rate and 101
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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
Page 49 and 50: 4. CREATING EXPERIENCES The MDA fra
Page 51 and 52: Play-styles are sets of isotopic ga
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
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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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Page 103 and 104: The Butcher persona would have quit
Page 105 and 106: ARTICLE 3 - WAVING EXPERIENCES: PLA
Page 107 and 108: types" [12]. Personas are construct
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Page 111 and 112: Crew actions schemas are: • Squad
Page 113 and 114: 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
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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
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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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Play-Persona: Modeling Player Behaviour in Computer Games

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