Diversifying dynamic difficulty adjustment agent by integrating player state models into Monte-Carlo tree search

Moon, Jaeyoung; Choi, Youjin; Park, Taehwa; Choi, JunDoo; Hong, Jin-Hyuk; Kim, Kyung Joong

doi:10.1016/j.eswa.2022.117677

Cited by 9 publications

(1 citation statement)

References 16 publications

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“…Additionally, the overall effect of difficulty level adjustment may be improved by using the proposed method to adjust the difficulty level periodically over the course of a trial instead of determining a single difficulty value that is used throughout the entire trial. Furthermore, the input-output relationship defined in this study can be applied to other methodologies, such as dynamic difficulty adjustment (DDA) [21,46] or Recurrent Neural Networks (RNN). Thus, in future research, we plan to compare the performance of the single prediction application of the proposed ML model presented in this work with a recurring prediction application of this model.…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning-Based Initial Difficulty Level Adjustment Method for Balance Exercise on a Trunk Rehabilitation Robot

Lee

Choi

Eizad

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Trunk rehabilitation exercises such as those for remediating core stability can help improve the seated balance of patients with weakness or loss of proprioception caused by diseases such as stroke, and aid the recovery of other functions such as gait. However, there has not yet been any reported method for automatically determining the parameters that define exercise difficulty on a trunk rehabilitation robot (TRR) based on data such as the patient's demographic information, balancing ability, and training sequence, etc. We have proposed a machine learning (ML)-based difficulty adjustment method to determine an appropriate virtual damping gain (Dvirtual) of the controller for the TRR's unstable training mode. Training data for the proposed system is obtained from 37 healthy young adults, and the trained ML model thus obtained is tested through experiments with a separate population of 25 healthy young adults. The leave-one-out cross validation results (37 subjects) from the training group for validation of the designed ML model showed 80.90% average accuracy (R2 score) for using the given information to predict the desired difficulty levels, which are represented by the level of balance performance quantified as Mean Velocity Displacement (MVD) of the center of pressure. Statistical analysis (Repeated measures analysis of variance) of subject performance also showed that ground truth difficulty levels from the training data and predicted difficulty levels did not differ significantly under any of the three exercise modes used in this study (Hard, Medium, and Easy), and the standard deviations were reduced by 16.39, 41.39, and 25.68%, respectively. Moreover, the Planar Deviation (PD) of the center of pressure, which was not the target parameter here, also showed results similar to the MVD, which indicates that the predicted Dvirtual affected the difficulty level of balance performance. Therefore, the proposed ML model-based difficulty adjustment method has potential for use with people who have varied balancing abilities.

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Section: Discussionmentioning

confidence: 99%