带绑定器的名词自动机学习
On Learning Nominal Automata with Binders
摘要：
我们研究了一个学习算法的名义自动机的背景下，一个经典的自动机的字母特征的名称。这类自动机捕获了名义正则语言;类似于经典语言理论，名义自动机已经被证明可以用绑定器来替换名义正则表达式。这些形式主义是服从抽象建模资源感知计算。本文提出了一种带绑定器的标称正则语言的学习算法。我们的算法推广了Angluin的L* 算法，相对于名义正则语言与绑定器。我们证明了算法的正确性，并研究了算法的理论复杂度。
英文摘要：
We investigate a learning algorithm in the context of nominal automata, an extension of classical automata to alphabets featuring names. This class of automata captures nominal regular languages; analogously to the classical language theory, nominal automata have been shown to characterise nominal regular expressions with binders. These formalisms are amenable to abstract modelling resource-aware computations. We propose a learning algorithm on nominal regular languages with binders. Our algorithm generalises Angluin's L* algorithm with respect to nominal regular languages with binders. We show the correctness and study the theoretical complexity of our algorithm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
