Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play 2018
DOI: 10.1145/3242671.3242706
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Towards Deep Player Behavior Models in MMORPGs

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 in balancing and error-coping accumulate. To tackle these challenges, we introduce deep player behavior models, applying machine learning techniques to individual, atomic decisionmaking strategies. We discuss their potential application in personalized challenges, autonomous game testing, human agent substitution, and online crime det… Show more

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Cited by 39 publications
(19 citation statements)
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“…In previous work, we were successful in showing that player modeling agents yield significantly higher motivation potential than heuristic opponents [36]. In addition, we contrasted different machine learning techniques in a player modeling study of the MMORPG Lineage 2 [33,35], showing that deep learning offers the highest individual prediction accuracies with the ability to reproduce playing sessions that closely resemble the original behavior, as well as offering the potential to differentiate between players. Based on this, we embedded DPBM into a long-term DDA evaluation about competing against agents of own behavior on a daily basis in the MMORPG AION [37], in which DPBM opponents were perceived to be significantly more engaging than traditional DDA opponents adjusted by heuristic parameter tuning.…”
Section: Related Workmentioning
confidence: 91%
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“…In previous work, we were successful in showing that player modeling agents yield significantly higher motivation potential than heuristic opponents [36]. In addition, we contrasted different machine learning techniques in a player modeling study of the MMORPG Lineage 2 [33,35], showing that deep learning offers the highest individual prediction accuracies with the ability to reproduce playing sessions that closely resemble the original behavior, as well as offering the potential to differentiate between players. Based on this, we embedded DPBM into a long-term DDA evaluation about competing against agents of own behavior on a daily basis in the MMORPG AION [37], in which DPBM opponents were perceived to be significantly more engaging than traditional DDA opponents adjusted by heuristic parameter tuning.…”
Section: Related Workmentioning
confidence: 91%
“…Based on insights about expressive data and suitable modeling techniques from our earlier work [35,36], we recorded all crucial player action decisions (attacking with -or switching to -a specific element and jumping) together with situational data from the current game state. After every level and for each player, the recorded behavioral data from all preceding levels was fed into a dedicated 24x10x10x9 feed-forward multi-layer perceptron with backpropagation and a logistic sigmoid activation function (cf.…”
Section: Deep Player Behavior Modelingmentioning
confidence: 99%
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“…For instance, creating bots with varied playstyles can be useful for game designers in early development stages, such as playtesting using bots [5,25]. Unfortunately, such bot behaviors have not been widely adopted in the game industry.…”
Section: Introductionmentioning
confidence: 99%