The purpose of this paper is to present an algorithm for inferring nondeterministic functional transducers. It has a lot in common with other well known algorithms such has RPNI and OSTIA. Indeed we will argue that this algorithm is a generalisation of both of them. Functional transducers are all those nondeterministic transducers whose regular relation is a function. Epsilon transitions as well as subsequential output can be erased for such machines, with the exception of output for empty string being lost. Learning partial functional transducers from negative examples is equivalent to learning total from positive-only data.

中文翻译：

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非确定性函数传感器推理算法

本文的目的是提出一种用于推断非确定性功能传感器的算法。它与其他众所周知的算法有很多共同点，例如 RPNI 和 OSTIA。事实上，我们会争辩说这个算法是它们两者的概括。功能转换器是所有那些规则关系是函数的非确定性转换器。对于此类机器，可以擦除 Epsilon 转换以及后续输出，但丢失空字符串的输出除外。从反例中学习部分功能转换器相当于从正数据中学习总数。