Towards Deep Player Behavior Models in MMORPGs (bibtex)
by J. Pfau, J.D. Smeddinck, R. Malaka
Abstract:
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 inbalancing 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 multi-player online role-playing game Lineage II depict a bench-mark 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.
Reference:
J. Pfau, J.D. Smeddinck, R. Malaka, "Towards Deep Player Behavior Models in MMORPGs", In , 2018.
Bibtex Entry:
@inproceedings{pfau2018,
    title = {Towards Deep Player Behavior Models in MMORPGs},
    doi = {10.1145/3242671.3242706},
    author = {Pfau, J. and Smeddinck, J.D. and Malaka, R.},
    month = oct,
    year = {2018},
    keywords = {easecrc_cognitive_arch_systems;Neural networks; deep learning; HMM; decision trees; games;player modeling; personalization; game testing; adaptive agents; dynamic difficulty adjustment},
    abstract = {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 inbalancing 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 multi-player online role-playing game Lineage II depict a bench-mark 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.}
}
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