Abstract
In this work, we aim to improve transparency and efficacy in human-robot collaboration by developing machine teaching algorithms suitable for groups with varied learning capabilities. While previous approaches focused on tailored approaches for teaching individuals, our method teaches teams with various compositions of diverse learners using team belief representations to address personalization challenges within groups. We investigate various group teaching strategies, such as focusing on individual beliefs or the group's collective beliefs, and assess their impact on learning robot policies for different team compositions. Our findings reveal that team belief strategies yield less variation in learning duration and better accommodate diverse teams compared to individual belief strategies, suggesting their suitability in mixed-proficiency settings with limited resources. Conversely, individual belief strategies provide a more uniform knowledge level, particularly effective for homogeneously inexperienced groups. Our study indicates that the teaching strategy's efficacy is significantly influenced by team composition and learner proficiency, highlighting the importance of real-time assessment of learner proficiency and adapting teaching approaches based on learner proficiency for optimal teaching outcomes.
Abstract (translated)
在这项工作中，我们旨在通过开发适用于具有不同学习能力的团队的人工智能教学算法，提高人机协作的透明度和效率。与之前针对个人进行定制教学的方法不同，我们的方法通过团队信念表示来教授具有不同学习能力的团队，以解决团队内个人化挑战。我们研究了各种团队教学策略，如关注个人信念或团队信念，并评估它们对不同团队组合学习机器人政策的影响。我们的研究结果表明，团队信念策略在学习持续时间上产生较小差异，并且比个人信念策略更好地适应多样化的团队，表明在有限资源的情况下，这些策略非常适合混合熟练度环境。相反，个人信念策略提供了一个更加均匀的知识水平，特别是对于经验相同或相似的团队来说更为有效。我们的研究表明，教学策略的有效性显著受到团队构成和 learners proficiency的影响，强调了根据 learners proficiency 实时评估学习效果以及根据 learners proficiency 调整教学方法以实现最优教学成果的重要性。
URL
https://arxiv.org/abs/2404.15472
