Error-awareness Accelerates Active Automata Learning

TL;DR

This paper introduces methods that significantly speed up active automata learning by leveraging error-awareness and domain knowledge, enabling more efficient modeling of complex systems.

Contribution

It adapts the AAL algorithm L# to incorporate varying degrees of error-awareness, improving scalability and efficiency in learning system models.

Findings

Methods accelerate learning by orders of magnitude with strong domain knowledge.

Achieves up to an order of magnitude speedup with limited domain knowledge.

Demonstrates effectiveness on challenging literature-based problems.

Abstract

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.