让机器人理解老师怎么想，减少教学误解。
Second-order Theory of Mind for Human Teachers and Robot Learners
- 机器人用二阶心智模型预测教师认知偏差
- 选择适配教师认知的反馈可降低教学负担
- 适合人机协作教学场景中的智能学习系统
不清晰或无益的学习者反馈会引发教师与学习者之间的错误认知，从而增加人类教师的认知负荷。例如，机器人的反馈可能导致教师误判学习者对学习目标的理解程度或学习方式。同时，学习者也可能误解教师对其任务知识和学习过程的看法。为减轻教学负担，学习者应提供能考虑教师对自身认知及学习目标看法的反馈。本文为人工智能学习者引入了二阶心智理论，将其感知到的理性作为教师与学习者间错误信念的来源建模，并探索了学习者如何通过选择反映教师信念的反馈来减轻教学负担并提升教师效能。
原文摘要 · Abstract (English)
Confusing or otherwise unhelpful learner feedback creates or perpetuates erroneous beliefs that the teacher and learner have of each other, thereby increasing the cognitive burden placed upon the human teacher. For example, the robot's feedback might cause the human to misunderstand what the learner knows about the learning objective or how the learner learns. At the same time -- and in addition to the learning objective -- the learner might misunderstand how the teacher perceives the learner's task knowledge and learning processes. To ease the teaching burden, the learner should provide feedback that accounts for these misunderstandings and elicits efficient teaching from the human. This work endows an AI learner with a Second-order Theory of Mind that models perceived rationality as a source for the erroneous beliefs a teacher and learner may have of one another. It also explores how a learner can ease the teaching burden and improve teacher efficacy if it selects feedback which accounts for its model of the teacher's beliefs about the learner and its learning objective.
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