仅从正例学习可解释的时间属性
Learning Interpretable Temporal Properties from Positive Examples Only
摘要：
我们考虑的问题，解释的时间行为的黑盒系统使用人类可解释的模型。为此，根据最近的研究趋势，我们依赖于确定性有限自动机（DFA）和线性时序逻辑（LTL）公式的基本但可解释的模型。与大多数现有的学习DFA和LTL公式的作品相比，我们只依赖于正面的例子。我们的动机是，负面的例子通常很难观察到，特别是从黑箱系统。为了只从正面例子中学习有意义的模型，我们设计了依赖于模型的简洁性和语言最小性作为正则化器的算法。为此，我们的算法采用两种方法：一个象征性的和一个反例引导的。虽然符号的方法利用了一个有效的编码语言最小的约束满足问题，反例引导的依赖于生成合适的否定的例子修剪搜索。这两种方法都为我们提供了有效的算法，并在学习模型上提供了理论保证。为了评估我们的算法的有效性，我们评估所有的合成数据。
英文摘要：
We consider the problem of explaining the temporal behavior of black-box systems using human-interpretable models. To this end, based on recent research trends, we rely on the fundamental yet interpretable models of deterministic finite automata (DFAs) and linear temporal logic (LTL) formulas. In contrast to most existing works for learning DFAs and LTL formulas, we rely on only positive examples. Our motivation is that negative examples are generally difficult to observe, in particular, from black-box systems. To learn meaningful models from positive examples only, we design algorithms that rely on conciseness and language minimality of models as regularizers. To this end, our algorithms adopt two approaches: a symbolic and a counterexample-guided one. While the symbolic approach exploits an efficient encoding of language minimality as a constraint satisfaction problem, the counterexample-guided one relies on generating suitable negative examples to prune the search. Both the approaches provide us with effective algorithms with theoretical guarantees on the learned models. To assess the effectiveness of our algorithms, we evaluate all of them on synthetic data.
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。
