具有误差意识的主动自动机学习
Error-awareness Accelerates Active Automata Learning
AI总结：
该研究提出利用误差意识提升主动自动机学习效率，通过不同知识水平的适应算法实现显著加速。
摘要：
主动自动机学习（AAL）算法可以通过与系统交互来学习其行为模型。主要挑战仍然是扩展到更大的模型，特别是在系统有大量可能输入的情况下。现代AAL算法即使在每个状态大多数输入导致错误的情况下也难以扩展。在各种具有挑战性的问题中，这些错误是可观察的，即它们会发出已知的错误输出。受这些问题的启发，我们研究更高效地学习这些系统的方法。此外，我们考虑了不同程度的关于哪些输入在哪些状态不是错误生成的知识。对于每种知识水平，我们提供了一种匹配的L#算法适应方案，以充分利用该领域知识。我们的实证评估表明，这些方法在具有强但现实领域知识的情况下，可以将学习加速数个数量级，而在有限领域知识的情况下，加速效果则为一个数量级。
英文摘要：
Active automata learning (AAL) algorithms can learn a behavioral model of a system from interacting with it. The primary challenge remains scaling to larger models, in particular in the presence of many possible inputs to the system. Modern AAL algorithms fail to scale even if, in every state, most inputs lead to errors. In various challenging problems from the literature, these errors are observable, i.e., they emit a known error output. Motivated by these problems, we study learning these systems more efficiently. Further, we consider various degrees of knowledge about which inputs are non-error producing at which state. For each level of knowledge, we provide a matching adaptation of the state-of-the-art AAL algorithm L# to make the most of this domain knowledge. Our empirical evaluation demonstrates that the methods accelerate learning by orders of magnitude with strong but realistic domain knowledge to a single order of magnitude with limited domain knowledge.
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。
