Play-Persona: Modeling Player Behaviour in Computer Games

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Within the last five years, instrumentation data — or game metrics as they are referred to in game development — has gained increasing attention in the game industry as a source of detailed information about player behavior in computer games [2]. Gameplay metrics are detailed numerical data extracted from the interaction of the player with the game using specialized monitoring software [3]. The application of machine learning on such data and the inference of playing patterns from the data can provide an alternative quantitative approach to and supplement traditional qualitative approaches to user and playability testing [4]. In this paper, we investigate dissimilar patterns of playing behavior in the popular commercial game title Tomb Raider: Underworld (TRU) using the principles of self-organization. The experiments presented are based on a data set derived from 1365 players, which completed the entire TRU game during November 2008. Data was collected via the EIDOS Metrics Suite (a game metrics logging system utilized by EIDOS). The data collection process is completely unobtrusive since data was gathered directly from the game engines of subjects playing TRU in their natural habitat (via the Xbox Live! web service) rather than in a laboratory setup. Six statistical features that correspond to high-level playing behaviors are extracted from the data. Information about game completion time, number of deaths, causes of death and help on demand actions is used for feature extraction. An initial analysis via k-means and Ward’s hierarchical clustering methods provides some first insight into the inner structure of the features. Then unsupervised learning via Emergent Self- Organizing Maps (ESOMs) [5] — an efficient visualization tool for large-scale self-organizing maps (SOMs) [6] — is used to identify dissimilar clusters (types) of playing behavior. The highest- performing ESOM network which is built on the six individual features of play projects four clusters of behavior, covering the vast majority of the sampled players. The four groups of behavior identified by self-organization are subsequently labeled in terms of the game mechanics and game design. This process ensures that the outcome of the analysis is in a form and terminology that is usable by the game designers evaluating if the players interact with the game in the way intended (given the specific input playing features) and deciding on whether and how to change the design in order to alter the behavior of the players. Unsupervised learning has been utilized for modeling player behavior in games (e.g. [7]); however, to the best of the authors’ knowledge, SOMs have not been applied for modeling high-level behaviors of players trained on data of completed games. Results showcase the effectiveness of 208
unsupervised learning in capturing dissimilar high-level playing behaviors (e.g. completion time) in the TRU game. Such behavior clusters can form the basis of further investigation in lower-levels of playing behavior (e.g. shooting accuracy in a specific level of the game). Because this study is based on large-scale data collection (1365 players) obtained from a major commercial game and via an industrial logging system, the findings of this study directly address the requirements for player behavior modeling in the game industry. Very few studies of academic game AI investigate actual commercial-standard games due to lack of source-code accessibility and even fewer examine the game as a whole. This often generates challenges with respect to generalization of the main findings to a real game production and with respect to directly addressing the needs of the game industry. 2. PLAYER MODELING User modeling is a broad field of research with numerous applications. In this section we will concentrate on user modeling approaches to games, namely player modeling [8], [9]. Among the few player modeling studies existent in the literature, quantitative models of players have been built to assist the learning of basic non-player character (NPC) behaviors (e.g. moving, shooting) in Quake II [10], [7], [11]. In those studies self-organizing maps [7], bayesian networks [11] and neural gas [10] approaches are utilized for clustering game-playing samples. Similarly, self-organizing maps have been used for clustering the trails (player waypoints) of users playing a simple level exploration game [12]. Viewing player modeling as an intermediate process of adaptive learning in games, Yannakakis and Maragoudakis [13] trained naïve bayesian models of Pac-Man players which infer values for parameters of an on-line neuro-evolutionary learning mechanism. The on-line learning mechanism was designed to maximize the entertainment value of the game by adjusting NPC behavior during play. Within the field of interactive narrative and AI in games, quantitative models of players — partially built on theoretical qualitative models — have been used to dynamically select the content of an interactive story [14]. Self-organizing maps — as a tool for player modeling — have been primarily used to generate a low-dimensional data map to assist training of NPC behaviors. For instance, the trained SOM can generate the desired outputs of a supervised learning approach [7] or form the action-state space of a reinforcement learning approach [15]. On that basis, Thurau et al. [7] utilize SOMs to lower the dimensionality of input data for training multi layered perceptrons that perform simple playing 209
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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
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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-
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
Page 177 and 178: a secondary variable together with
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: ARTICLE 9 - PLAYER MODELING USING S
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
Page 245 and 246: ARTICLE 12 - ANALYZING USER BEHAVIO
Page 247 and 248: elated to player-game interaction a
Page 249 and 250: 10. Kuniavsky, M.: Observing the Us
Page 251 and 252: ARTICLE 13 - PATTERNS OF PLAY: PLAY
Page 253 and 254: Play-personas are here introduced a
Page 255 and 256: It is only natural that game design
Page 257 and 258: 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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