Abstract
We study the following gray-box learning problem: Given the serial composition of two Mealy machines A and B, where A is known and B is unknown, the goal is to learn a model of B using only output and equivalence queries on the composed machine. We introduce an algorithm that solves this problem, using at most |B| equivalence queries, independently of the size of A. We discuss its efficient implementation and evaluate the algorithm on existing benchmark sets as well as randomly-generated machines.
Cite
CITATION STYLE
Abel, A., & Reineke, J. (2016). Gray-box learning of serial compositions of mealy machines. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9690, pp. 272–287). Springer Verlag. https://doi.org/10.1007/978-3-319-40648-0_21
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