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 Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play, pp. 381-392, 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.},
    booktitle={Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play},
pages={381--392},
    month = {oct},
    year = {2018},
    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.},
url = {https://eprint.ncl.ac.uk/file_store/production/253833/BAAABDF6-7C8A-49F5-9670-32EB0F2AC44C.pdf},
keywords = {easecrc_cognitive_arch_systems}
}
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