We study the learnability of {symbolic finite state automata}, a model shown useful in many applications in software verification. The state-of-the-art literature on this topic follows the {query learning} paradigm, and so far all obtained results are positive. We provide a necessary condition for efficient learnability of SFAs in this paradigm, from which we obtain the first negative result. Most of this work studies learnability of SFAs under the paradigm of {identification in the limit using polynomial time and data}. We provide a sufficient condition for efficient learnability of SFAs in this paradigm, as well as a necessary condition, and provide several positive and negative results.

中文翻译：

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推断符号自动机

我们研究了{符号有限状态自动机}的可学习性，该模型在软件验证的许多应用中显示出有用。关于这个主题的最新文献遵循{查询学习}范式，到目前为止所有获得的结果都是积极的。我们为这种范式中 SFA 的有效学习提供了必要条件，从中我们获得了第一个负面结果。这项工作的大部分内容是在{使用多项式时间和数据的极限识别}范式下研究 SFA 的可学习性。我们为该范式中 SFA 的有效学习性提供了充分条件和必要条件，并提供了几个正面和负面的结果。