Gray-Box Learning of Serial Compositions of Mealy Machines

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9690)


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.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceSaarland UniversitySaarbückenGermany

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