Lookup NU author(s): Dr Jan Smeddinck
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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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.
Author(s): Pfau J, Smeddinck JD, Malaka R
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: CHI PLAY 2018 - Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play
Year of Conference: 2018
Online publication date: 23/10/2018
Acceptance date: 02/04/2018
Date deposited: 18/01/2019
Publisher: Association for Computing Machinery, Inc
Library holdings: Search Newcastle University Library for this item