通过感知错误提升自动机学习效率，显著加速模型构建。
Error-awareness Accelerates Active Automata Learning
- 引入错误感知机制，利用可观察的错误输出优化学习路径。
- 在具备真实领域知识时，学习速度提升数个数量级。
- 适用于对输入错误有部分了解的系统建模任务。
主动自动机学习（AAL）算法可通过与系统交互来学习其行为模型。主要挑战在于扩展到更大规模模型，尤其是在存在大量可能输入的情况下。即使在每个状态中多数输入会导致错误，现代AAL算法仍难以扩展。文献中的多个难题中，这些错误是可观察的，即会发出已知的错误输出。为此，我们研究如何更高效地学习此类系统，并考虑对哪些输入在哪些状态下不会产生错误的不同程度的知识。针对每种知识水平，我们对最先进的AAL算法L#进行了适配，以充分利用该领域知识。实验表明，当具备强但现实的领域知识时，方法可使学习加速数个数量级；在仅有有限领域知识时，仍可实现约一个数量级的加速。
原文摘要 · Abstract (English)
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.
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