Enemy Within: Long-term Motivation Effects of Deep Player Behavior Models for Dynamic Difficulty Adjustment

Pfau, J; Smeddinck, JD; Malaka, R

doi:10.1145/3313831.3376423

Enemy Within: Long-term Motivation Effects of Deep Player Behavior Models for Dynamic Difficulty Adjustment

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, 2020.

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Abstract

Balancing games and producing content that remains interesting and challenging is a main cost factor in the design and maintenance of games. Dynamic difficulty adjustments (DDA) can successfully tune challenge levels to player abilities, but when implemented with classic heuristic parameter tuning (HPT) often turns out to be very noticeable, e.g. as "rubber-banding". Deep learning techniques can be employed for deep player behavior modeling (DPBM), enabling more complex adaptivity, but effects over frequent and longer-lasting game engagements, as well as how it compares to HPT has not been empirically investigated. We present a situated study of the effects of DDA via DPBM as compared to HPT on intrinsic motivation, perceived challenge and player motivation in a real-world MMORPG. The results indicate that DPBM can lead to significant improvements in intrinsic motivation and players prefer game experience episodes featuring DPBM over experience episodes with classic difficulty management.