Play-Persona: Modeling Player Behaviour in Computer Games

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7.2 ESOM Fig. 4. Rectangular lattice map organized in a toroid shape; SOM size: 50 X 100 neurons. As previously mentioned in Section 5, a toroid-shaped ESOM structured in a sheet grid is utilized to identify classes of playing behavior in TRU. Fig. 4 depicts the 50 100 neuron structure used in the experiments presented in this paper. The number of 5000 neurons is chosen to provide a good compromise between training performance, computational effort and resolution detail of the map. The toroid shape of the map is selected due to border effects observed in training attempts with 2- dimensional sheet maps. 1) ESOM training: The weight vector of the ESOM is randomly initialized using a Gaussian distribution; the mean and standard deviation of the distribution are set to the respective values of the corresponding input feature. The initial neighborhood size is set to 25 which linearly drops to 1 while the weighting neighbor function, h, used is the Gaussian kernel. The learning rate is set to 0.7 but is decreased linearly during training reaching the value of 0.1 at the end of the 100 training epochs used. The training samples are presented in a randomly permuted order at each epoch of the algorithm. In order to minimize the effect of non-deterministic selection of initial 218
weight values we repeat the training 20 times — using dissimilar initial weight vectors — and select the ESOM with the smallest average quantization error (see Section VI). The highest-performing ESOM that is examined in the remained of this paper has a quantization error of 0.038 and a corresponding topographic error of 0.005. As a baseline performance to compare against, the mean quantization and topographic error for 10 randomly generated ESOMs equals 0.1744 and 0.9983 respectively. 2) ESOM visualization: The training data can be clustered by observation of the best performing ESOM. The U-matrix depicted in Fig. 5(a) is a visualization of the local distance structure in the data placed onto the two-dimensional map. The average distance value between each neuron’s weight vector and the weight vectors of its immediate neighbors corresponds to the height of that neuron in the U-matrix (positioned at the map coordinates of the neuron). Thus, Umatrix values are large in areas where no or few data points reside, creating mountain ranges for cluster boundaries. On the other hand, visualized valleys indicate clusters of data since small U- matrix values are observed in areas where the data space distances of neurons are small. Distance based map visualizations (e.g. U-matrix) usually work well for clearly separated clusters; however, problems may occur with overlapping clusters. Density-based SOM visualizations display the density of the data onto the map space via the best-matching neurons. The P-matrix (see Fig. 5(b)) displays the local density measures with Pareto Density Estimation [21]. Neurons with large P- matrix values are located in dense regions of the input vector space and, therefore, areas with height P-matrix values indicate clusters in the data. 3) Player Types: The two map visualizations are complementary and used for cluster identification within the TRU data. Four main classes (player types) can be identified as depicted in Fig. 5(a) and Fig. 5(b). The best-matching neurons for all 1365 input vector samples are also represented with small squares of varying color — different colors correspond to different clusters of the best matching neurons. On the same basis, Table I presents the number of observations (i.e. players completed TRU) and percent of neurons belonging to each of the four clusters. Note that 87 (6.37% of the sample) players were not assigned to any cluster since their best matching neurons are placed in cluster borders of the ESOM (see Fig. 5). Fig. 6 illustrates the corresponding component planes of the ESOM. A component plane projects the relative distribution of one input data vector component (i.e. input vector dimension) to the ESOM. In the grayscale illustration of those values, white areas represent relatively small values while dark 219
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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-
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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-
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
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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: populated middle-left cluster (Fig.
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
Page 259 and 260: TRU is the eighth installment in th
Page 261 and 262: gameplay, descriptiveness of player
Page 263 and 264: In the case of TRU, the three param
Page 265 and 266: into which sections of the levels t
Page 267 and 268: Runners: This group completes the g
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17. Iser, W.: The Implied Reader. J
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