发表机构
University of Kassel（卡塞尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对教学反馈场景，提出用正则树语言有限刻画确定性有限自动机的全部修正，并借助过滤器筛选满足教学约束的修正，实现个性化反馈生成。
AI 中文摘要
受教学应用启发，我们研究了计算所有修正的问题，这些修正将一个有限自动机转换为识别给定正则语言L的自动机。我们证明，对于确定性有限自动机，所有修正的集合可以有限地刻画为正则树语言。该构造基于对所有识别L的确定性有限自动机的树编码，并扩展为修正树，使单个修正及其引发的编辑操作显式化。利用正则树语言的封闭性质，我们引入了所谓过滤器，用于选择满足教学约束的修正，从而能够从学生提交中推导出个性化反馈。
英文摘要
Motivated by educational applications, we study the problem of computing all corrections that transform a finite automaton into one recognizing a given regular language L. We show that for deterministic finite automata the set of all corrections can be finitely characterized as a regular tree language. The construction is based on a tree encoding of all deterministic finite automata recognizing L, which is extended to correction trees that make individual corrections and their induced edit-operations explicit. Leveraging the closure properties of regular tree languages, we introduce so-called filters for selecting corrections satisfying didactic constraints, enabling the derivation of individualized feedback from student submissions.
CommentsIn Proceedings AFL 2026, arXiv:2608.23071
Journal refEPTCS 451, 2026, pp. 75-89
