Session: Paper Presentation CHI PLAY 2018, October 28–31, 2018, Melbourne, VIC, Australia Towards Deep Player Behavior Models in MMORPGs Johannes Pfau Jan David Smeddinck Rainer Malaka Digital Media Lab, TZI Open Lab, School of Comp. Digital Media Lab, TZI University of Bremen Newcastle University University of Bremen Bremen, Germany Newcastle upon Tyne, UK Bremen, Germany
[email protected] [email protected] [email protected] ABSTRACT issues with large-scale systems, and cheating or other unethical Due to a steady increase in popularity, player demands for behavior. We approach the closing of multiple unsolved gaps video game content are growing to an extent at which consis- in these areas of concern for game research and development tency and novelty in challenges are hard to attain. Problems in based on an uncommon building block: deep player behav- balancing and error-coping accumulate. To tackle these chal- ior modeling (DPBM). We discuss the potential of DPBM lenges, we introduce deep player behavior models, applying with regard to the challenges indicated above. To establish machine learning techniques to individual, atomic decision- apt representation techniques we also explore the potential of making strategies. We discuss their potential application in different machine learning techniques for player modeling in personalized challenges, autonomous game testing, human massively multiplayer online role-playing games (MMORPGs) agent substitution, and online crime detection. Results from and implement a pilot study which provides a first data set a pilot study that was carried out with the massively multi- and enables the comparison between selected models. We player online role-playing game Lineage II depict a bench- hypothesize that different advantages can be attained from mark between hidden markov models, decision trees, and deep Hidden Markov Models (HMMs), decision trees (DTs) and learning.