论文标题
学习自动机和传感器：一种分类方法
Learning automata and transducers: a categorical approach
论文作者
论文摘要
在本文中，我们介绍了一种对单词学习自动机的绝对方法，从$ l^*$ - Angluin算法的意义上讲。这产生了一种新的通用$ l^*$ - 像算法一样，可以实例化用于学习确定性自动机，自动机对字段加权以及随后的传感器。我们的算法的通用性质是通过采用一种方法，即自动机是代表单词到“计算类别”的特定类别的函子。我们确定，产生最小自动机的存在的足够特性（在上一篇论文中披露了），结合了与终止相对于终止的一些其他假设，请确保我们的通用算法的正确性。
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.
