The monotonous nature of repetitive cognitive training may cause losing interest in it and dropping out by older adults. This study introduces an adaptive technique that enables a Socially Assistive Robot (SAR) to select the most appropriate actions to maintain the engagement level of older adults while they play the serious game in cognitive training. The goal is to develop an adaptation strategy for changing the robot's behaviour that uses reinforcement learning to encourage the user to remain engaged. A reinforcement learning algorithm was implemented to determine the most effective adaptation strategy for the robot's actions, encompassing verbal and nonverbal interactions. The simulation results demonstrate that the learning algorithm achieved convergence and offers promising evidence to validate the strategy's effectiveness.

翻译：重复性认知训练的单调特性可能导致老年人失去兴趣并退出训练。本研究提出一种自适应技术，使社交辅助机器人（SAR）能够在老年人进行严肃游戏认知训练时，选择最恰当的行为以维持其参与度。研究目标在于开发基于强化学习的机器人行为自适应策略，通过激励用户持续参与训练。通过实现强化学习算法，确定涵盖语言与非语言交互的机器人行为最优自适应策略。仿真结果表明，该学习算法实现了收敛，并为验证该策略的有效性提供了有力证据。