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arXiv 2010.13675cs.FL
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学习自动机和转换器:分类方法

Learning automata and transducers: a categorical approach

Thomas Colcombet, Daniela Petrişan, Riccardo Stabile

摘要:

在本文中,我们提出了一个分类的方法来学习自动机的话,在这个意义上的$L^*$算法的Angluin。这产生了一个新的通用的$L^*$类算法,可以实例化学习确定性自动机,自动机加权字段,以及顺序传感器。我们的算法的一般性质是通过采用一种方法,其中自动机只是函子从一个特定的类别表示的话,以“计算类别”。我们建立了足够的属性产生最小自动机的存在(这是在以前的文件中披露),结合一些额外的假设相对于终止,确保我们的通用算法的正确性。

英文摘要:

In this paper, we present a categorical approach to learning automata over words, in the sense of the $L^*$-algorithm of Angluin. This yields a new generic $L^*$-like algorithm which can be instantiated for learning deterministic automata, automata weighted over fields, as well as subsequential transducers. The generic nature of our algorithm is obtained by adopting an approach in which automata are simply functors from a particular category representing words to a "computation category". We establish that the sufficient properties for yielding the existence of minimal automata (that were disclosed in a previous paper), in combination with some additional hypotheses relative to termination, ensure the correctness of our generic 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。

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