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Evolutionary Algorithms for a Better Gaming Experience in Rehabilitation Robotics

By Kleber O. Andrade, Ricardo C. Joaquim, Glauco A. P. Caurin, Marcio K. Crocomo
Special Issue: Deep Learning, Ubiquitous and Toy Computing, [Vol. 16, No. 2]

DOI: 10.1145/3180657

This article proposes the use of two evolutionary algorithms (EAs) to the dynamic difficulty adjustment (DDA) of a serious game in the rehabilitation robotics application. DDA occurs in runtime for a better user experience with a game. This approach is used to improve the quality of the game experience and to avoid boredom or frustration for players with severe limitations imposed by pathologies such as stroke, cerebral palsy, and spinal cord injuries. The first EA solves the game adjustment problem, changing the game difficulty according to the player’s skill, and the purpose of the second EA is to adjust the coefficients of the first EA’s objective function so that it can work in a more effective way. To do so, the second EA uses results of game matches against simulated player profiles. The results shows that the presented method was able to identify a set of coefficients that allows the first EA to correctly adjust the difficulty level for all six tested player profiles.

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