Abstract
We present a unifying formalization of active automata learning algorithms in the MAT model, including a new, efficient, and simple technique for the analysis of counterexamples during learning: \(L^{\!\lambda }\) is the first active automata learning algorithm that does not add sub-strings of counterexamples to the underlying data structure for observations but instead performs black-box search and partition refinement. We analyze the worst case complexity in terms of membership queries and equivalence queries and evaluate the presented learning algorithm on benchmark instances from the Automata Wiki, comparing its performance against efficient implementations of some learning algorithms from LearnLib.
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Notes
- 1.
Automata Wiki: ru.nl and [13].
- 2.
The \(L^{\!\lambda }\) algorithm may (where possible) use its underlying data structure for determining output values or resort to membership queries and a cache.
- 3.
A more efficient tree-based version of is used in the evaluation. Both variants are implemented in LearnLib for reference.
- 4.
Replicating results from a recent paper by Frits and coauthors [21], we count input symbols and resets instead of inputs symbols in this series.
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Howar, F., Steffen, B. (2022). Active Automata Learning as Black-Box Search and Lazy Partition Refinement. In: Jansen, N., Stoelinga, M., van den Bos, P. (eds) A Journey from Process Algebra via Timed Automata to Model Learning . Lecture Notes in Computer Science, vol 13560. Springer, Cham. https://doi.org/10.1007/978-3-031-15629-8_17
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