论文标题
了解知识不完美的教学的力量和局限性
Understanding the Power and Limitations of Teaching with Imperfect Knowledge
论文作者
论文摘要
机器教学研究教师与学生/学习者之间的互动，教师为学习者选择培训示例以学习特定任务。典型的假设是，教师对任务有完美的了解 - 这种知识包括知道所需的学习目标，拥有学习者使用的确切任务表示形式，并且知道捕获学习者学习动态的参数。受到机器教学在教育中的实际应用的启发，我们考虑了教师知识有限且嘈杂的环境，我们研究的关键研究问题如下：教师什么时候成功或失败，使用其不完美的知识有效地教授学习者？我们通过展示与不完美知识的联系来回答这个问题，在构建最佳教学集时，教师对相应的机器教学问题的解决方案。我们的结果对于为现实世界应用设计可靠的教学算法具有重要意义。
Machine teaching studies the interaction between a teacher and a student/learner where the teacher selects training examples for the learner to learn a specific task. The typical assumption is that the teacher has perfect knowledge of the task---this knowledge comprises knowing the desired learning target, having the exact task representation used by the learner, and knowing the parameters capturing the learning dynamics of the learner. Inspired by real-world applications of machine teaching in education, we consider the setting where teacher's knowledge is limited and noisy, and the key research question we study is the following: When does a teacher succeed or fail in effectively teaching a learner using its imperfect knowledge? We answer this question by showing connections to how imperfect knowledge affects the teacher's solution of the corresponding machine teaching problem when constructing optimal teaching sets. Our results have important implications for designing robust teaching algorithms for real-world applications.
