Towards deep player behavior models in MMORPGs

Pfau, J; Smeddinck, JD; Malaka, R

doi:10.1145/3242671.3242706

Towards deep player behavior models in MMORPGs

Lookup NU author(s): Dr Jan Smeddinck ORCiD

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This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by Association for Computing Machinery, Inc, 2018.

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Abstract

Âl’ 2018 Copyright is held by the owner/author(s). Due to a steady increase in popularity, player demands for video game content are growing to an extent at which consistency and novelty in challenges are hard to attain. Problems in balancing and error-coping accumulate. To tackle these challenges, we introduce deep player behavior models, applying machine learning techniques to individual, atomic decision-making strategies. We discuss their potential application in personalized challenges, autonomous game testing, human agent substitution, and online crime detection. Results from a pilot study that was carried out with the massively multiplayer online role-playing game Lineage II depict a benchmark between hidden markov models, decision trees, and deep learning. Data analysis and individual reports indicate that deep learning can be employed to provide adequate models of individual player behavior with high accuracy for predicting skill-use and a high correlation in recreating strategies from previously recorded data.