Faculty of Multimedia, Tabriz Islamic Art University, Tabriz, Iran
Abstract
Research has shown that the design of a rehabilitation task as a therapeutic game can result in a motivating rehabilitation environment. Generally, the difficulty level of a therapeutic game is regulated manually by a therapist. However, home-based rehabilitation games require a technique for automatic difficulty adjustment. This paper proposes a personalized difficulty adjustment technique for a rehabilitation game that automatically regulates difficulty settings based on a patient’s skills in real-time. To this end, ideas from reinforcement learning are used to dynamically adjust the difficulty of a game. We show that difficulty adjustment is a multiple-objective problem, in which some objectives might be evaluated at different periods. To address this problem, we propose and use Multiple-Periodic Reinforcement Learning that makes it possible to evaluate different objectives of difficulty adjustment in separate periods. The results of experiments show that this technique outperforms traditional Multiple-Objective Reinforcement Learning in terms of user satisfaction parameters.
Sekhavat, Y. A. and Zarei, H. (2018). Dynamic Difficulty Adjustment of Rehabilitation Games using Reinforcement Learning. TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, 48(1), 61-70.
MLA
Sekhavat, Y. A. , and Zarei, H. . "Dynamic Difficulty Adjustment of Rehabilitation Games using Reinforcement Learning", TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, 48, 1, 2018, 61-70.
HARVARD
Sekhavat, Y. A., Zarei, H. (2018). 'Dynamic Difficulty Adjustment of Rehabilitation Games using Reinforcement Learning', TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, 48(1), pp. 61-70.
CHICAGO
Y. A. Sekhavat and H. Zarei, "Dynamic Difficulty Adjustment of Rehabilitation Games using Reinforcement Learning," TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, 48 1 (2018): 61-70,
VANCOUVER
Sekhavat, Y. A., Zarei, H. Dynamic Difficulty Adjustment of Rehabilitation Games using Reinforcement Learning. TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, 2018; 48(1): 61-70.
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