We present a study focused on constructing models of players for the major commercial title Tomb Raider: Underworld (TRU). Emergent self-organizing maps are trained on high-level playing behavior data obtained from 1365 players that completed the TRU game. The unsupervised learning approach utilized reveals four types of players which are analyzed within the context of the game. The method can be applied directly in other games. For more info see: http://andersdrachen.files.wordpress.com/2011/01/tombraider_modeling_ieeecig.pdf

1. Player Behavior andPlay ExperienceChristian Thurau, Anders Drachen

2. Whoweare Christian Thurau, Fraunhofer IAIS Anders Drachen, Aalborg University Whatwe do

3. Player behavior Player behavior - definition: Everything a playerdoes in the game Moving an avatar Interacting with otherplayers Exploring an environment Assigningorders to units Navigating a storyline Etc.

4. Behavior and PX Behavior is relevant whenconsidering PX: Behavioranalysisinforms PX evaluations Behavior analysis provides evidence on PX problems Lack of progress Interference by other players No attention to surroundings GUI issues Behavioral analysis can be carried out at multiple scales – from one player to millions Distilling behavior into classes provides the means to detect unwanted behavior and address the root causes (e.g. archetype analysis) Testing and refining game design

5. User behavior Behavioranalysis: Recent complement to GUR methods: Usabilitytesting: Can the useroperate the controls? Playabilitytesting: Is the userhaving a goodexperience? Behavioranalysis: What is the userdoingwhileplaying?

6. User behavior Behaviortraditionallyexploredusingobservation and video capture. Games todaycanbecomplex -> challengestraditional GUR methods Enter: game telemetry Used in general IT sector for 20+ years – only a fewyearswidespreaduse in games (acrossdisciplines – AI, storytelling systems, design…)

7. User behavior Game telemetry is anythingthatcanberecorded from a game by an application! Player movement Firing weapons & usingabilities Information flow betweenplayers Measures of revenue Social networkbetweenplayers GUI interaction Game economybehavior ….

8. User behavior Game telemetry data: Highlydetailed Large or small samples Unobtrusive Canbecombinedwithqualitativemethods Answers ”what” and ”who” in game design Inferenceonly for ”why” – onlyindirect info on PX (usually … - smart people in AI arebuilding models for predicting PX and adapting games in real-time)

9. Gettingtelemetry data

10. Game telemetry

12. User behavior Telemetry notably widelyused for online social games Facebook games MMOGs Virtual worlds Casual games These games have a long lifetime = important to monitor usercommunity Evaluatedynamics in usercommunity Detectdisruptiveuserbehavior

13. User behavior Metricsuse in other game genres catchingup Industry racing to adoptmethods - companieshiring All major publishersrunninginitiatives 250+ members in the IGDA GUR SIG 2ndGUR summit: 70+ participants Specializedvendors (e.g. game analytics, kontagent) Exponentialincrease in research publications Strongindustry-academiacollaborations First book on the way (spring 2012)

14. Userbehavior Implications for research and development: The promise ofBig Data -andBig Depth Populations not samples Wide range of applications Measuringhowusersinteract with games and eachother Combiningmetrics with other measures for in-depthuser studies – notably PX

15. Player BehaviorClassification

16. Patterns of play Player behaviorclassificationvia game telemetry– aims: Distillcomplex datasets to find patterns of behavior[data mining] Debugging the playingexperience Comparingbehavior with design intent Optimization of game design Adaptation: Real-time dynamic adaptation to player type

17. Patterns of play Fundamental challenge: reducedimensionality Can have thousands of behavioral variables (features) Find the mostimportantbehavioral variables and classifyplayersaccording to these Multiple methodsfor doingthis – all require a human component (deciding the number of classes!) Lack ofcomparison of methods

18. Patterns of play Wecompared: K-meansclustering C-means, Non-Negative Matrix Factorization Principal Components Analysis Archetype Analysis Otherapproaches: e.g. self-organizingmaps Common - usedbefore in behavior analysis New – from economics

19. Patterns of play Evaluated70k playersof World of Warcraft Substantialvariationsin the resultsoffered by the differentmethods (!) Differentnumber of classes Differentproperty distribution in classes Clear challenges to behavioralclassification Scalingeffects Data types vs. algorithm Potential temporal effects (time-series analysis etc.)

20. Thank you